├── assets ├── face_mean.npy ├── face_std.npy ├── example_sentence.wav └── eye_keypoints.txt ├── supplemental_video.png ├── supplementary_material.pdf ├── CONTRIBUTING.md ├── training ├── dataset.py ├── train_step2.py ├── train_step1.py ├── trainer.py └── forwarder.py ├── README.md ├── CODE_OF_CONDUCT.md ├── animate_face.py ├── utils ├── renderer.py ├── helpers.py └── multiface2meshtalk.py ├── models ├── vertex_unet.py ├── encoders.py └── context_model.py └── LICENSE /assets/face_mean.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/facebookresearch/meshtalk/HEAD/assets/face_mean.npy -------------------------------------------------------------------------------- /assets/face_std.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/facebookresearch/meshtalk/HEAD/assets/face_std.npy -------------------------------------------------------------------------------- /supplemental_video.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/facebookresearch/meshtalk/HEAD/supplemental_video.png -------------------------------------------------------------------------------- /supplementary_material.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/facebookresearch/meshtalk/HEAD/supplementary_material.pdf -------------------------------------------------------------------------------- /assets/example_sentence.wav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/facebookresearch/meshtalk/HEAD/assets/example_sentence.wav -------------------------------------------------------------------------------- /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | # Contributing to meshtalk 2 | We want to make contributing to this project as easy and transparent as 3 | possible. 4 | 5 | ## Pull Requests 6 | We actively welcome your pull requests. 7 | 8 | 1. Fork the repo and create your branch from `master`. 9 | 2. If you've added code that should be tested, add tests. 10 | 3. If you've changed APIs, update the documentation. 11 | 4. Ensure the test suite passes. 12 | 5. Make sure your code lints. 13 | 6. If you haven't already, complete the Contributor License Agreement ("CLA"). 14 | 15 | ## Contributor License Agreement ("CLA") 16 | In order to accept your pull request, we need you to submit a CLA. You only need 17 | to do this once to work on any of Facebook's open source projects. 18 | 19 | Complete your CLA here: 20 | 21 | ## Issues 22 | We use GitHub issues to track public bugs. Please ensure your description is 23 | clear and has sufficient instructions to be able to reproduce the issue. 24 | 25 | Facebook has a [bounty program](https://www.facebook.com/whitehat/) for the safe 26 | disclosure of security bugs. In those cases, please go through the process 27 | outlined on that page and do not file a public issue. 28 | 29 | ## License 30 | By contributing to meshtalk, you agree that your contributions will be licensed 31 | under the LICENSE file in the root directory of this source tree. 32 | -------------------------------------------------------------------------------- /training/dataset.py: -------------------------------------------------------------------------------- 1 | """ 2 | Copyright (c) Facebook, Inc. and its affiliates. 3 | All rights reserved. 4 | This source code is licensed under the license found in the 5 | LICENSE file in the root directory of this source tree. 6 | """ 7 | 8 | import torch as th 9 | 10 | 11 | class DataReader: 12 | def __init__( 13 | self, 14 | segment_length: int = 32, 15 | n_vertices: int = 6172, 16 | audio_length: int = 16000 17 | ): 18 | """ 19 | TODO: this is only a placeholder loading zeros. Implement the dataloader for your dataset here. 20 | :param segment_length: length of an animated segment used for training. MeshTalk used 32 frames long segments. 21 | :param n_vertices: number of vertices in mesh 22 | :param audio_length: number of samples in provided audio. Note that audio is assumed to be at 16kHz 23 | """ 24 | self.segment_length = segment_length 25 | self.n_vertices = n_vertices 26 | self.audio_length = audio_length 27 | self.dataset_size = 100 # placeholder for dataset size 28 | self.current_idx = 0 29 | 30 | def __len__(self): 31 | return self.dataset_size 32 | 33 | def __iter__(self): 34 | return self 35 | 36 | def __getitem__(self, idx): 37 | """ 38 | :param idx: pointer to dataset element to be read 39 | :return: template: V x 3 tensor containing neutral face template 40 | geom: segment_length x V x 3 tensor containing animated segment for same subject as template mesh 41 | audio: segment_length x audio_length tensor containing the input audio for each frame of the segment 42 | """ 43 | template = th.zeros(self.n_vertices, 3) 44 | geom = th.zeros(self.segment_length, self.n_vertices, 3) 45 | audio = th.zeros(self.segment_length, self.audio_length) 46 | return { 47 | "template": template, 48 | "geom": geom, 49 | "audio": audio 50 | } 51 | -------------------------------------------------------------------------------- /training/train_step2.py: -------------------------------------------------------------------------------- 1 | """ 2 | Copyright (c) Facebook, Inc. and its affiliates. 3 | All rights reserved. 4 | This source code is licensed under the license found in the 5 | LICENSE file in the root directory of this source tree. 6 | """ 7 | 8 | import os 9 | import numpy as np 10 | import torch as th 11 | from models.encoders import MultimodalEncoder 12 | from models.context_model import ContextModel 13 | from training.dataset import DataReader 14 | from training.forwarder import CategoricalAutoregressiveForwarder 15 | from training.trainer import Trainer 16 | 17 | 18 | config = { 19 | "artifacts_dir": "artifacts_dir", 20 | "vertex_mean": "assets/face_mean.npy", 21 | "vertex_std": "assets/face_std.npy", 22 | "dataloader_workers": 4, 23 | "expression_space": { 24 | "classes": 128, 25 | "heads": 16, 26 | }, 27 | "train": { 28 | "learning_rate": 0.0001, 29 | "batch_size": 8, 30 | "segment_length": 64, 31 | "max_iterations": 15_000, 32 | "decrease_lr_iters": [10_000], 33 | "save_frequency": 5_000, 34 | "loss_terms": ["cross_entropy"], 35 | "loss_weights": {"cross_entropy": 1.0}, 36 | }, 37 | } 38 | 39 | os.system(f"mkdir -p {config['artifacts_dir']}") 40 | 41 | # load mean and stddev 42 | mean = th.from_numpy(np.load(config["vertex_mean"])) 43 | stddev = th.from_numpy(np.load(config["vertex_std"])) 44 | 45 | # define train and validation dataset 46 | train_dataset = DataReader(segment_length=64) 47 | val_dataset = DataReader(segment_length=64) 48 | 49 | encoder = MultimodalEncoder(classes=config["expression_space"]["classes"], 50 | heads=config["expression_space"]["heads"], 51 | expression_dim=128, 52 | audio_dim=128, 53 | n_vertices=train_dataset.n_vertices, 54 | mean=mean, 55 | stddev=stddev 56 | ) 57 | encoder.load(config["artifacts_dir"]) 58 | context_model = ContextModel(classes=config["expression_space"]["classes"], 59 | heads=config["expression_space"]["heads"], 60 | audio_dim=128 61 | ) 62 | print(f"context_model: {context_model.num_trainable_parameters()} trainable parameters") 63 | 64 | # train 65 | forwarder = CategoricalAutoregressiveForwarder(config, encoder, context_model) 66 | trainer = Trainer(config, forwarder, train_dataset, val_dataset) 67 | trainer.train() 68 | -------------------------------------------------------------------------------- /training/train_step1.py: -------------------------------------------------------------------------------- 1 | """ 2 | Copyright (c) Facebook, Inc. and its affiliates. 3 | All rights reserved. 4 | This source code is licensed under the license found in the 5 | LICENSE file in the root directory of this source tree. 6 | """ 7 | 8 | import os 9 | import numpy as np 10 | import torch as th 11 | from models.vertex_unet import VertexUnet 12 | from models.encoders import MultimodalEncoder 13 | from training.dataset import DataReader 14 | from training.forwarder import DiscreteExpressionForwarder 15 | from training.trainer import Trainer 16 | 17 | 18 | config = { 19 | "artifacts_dir": "artifacts_dir", 20 | "vertex_mean": "assets/face_mean.npy", 21 | "vertex_std": "assets/face_std.npy", 22 | "dataloader_workers": 4, 23 | "expression_space": { 24 | "classes": 128, 25 | "heads": 16, 26 | }, 27 | "train": { 28 | "learning_rate": 0.0001, 29 | "batch_size": 8, 30 | "segment_length": 32, 31 | "max_iterations": 50_000, 32 | "decrease_lr_iters": [40_000], 33 | "save_frequency": 10_000, 34 | "weight_decay": 0.001, 35 | "loss_terms": ["recon", "landmarks", "modality_crossing"], 36 | "loss_weights": {"recon": 1.0, "landmarks": 1.0, "modality_crossing": 1.0}, 37 | }, 38 | } 39 | 40 | os.system(f"mkdir -p {config['artifacts_dir']}") 41 | 42 | # load mean, stddev, and masks 43 | mean = th.from_numpy(np.load(config["vertex_mean"])) 44 | stddev = th.from_numpy(np.load(config["vertex_std"])) 45 | mouth_mask = np.loadtxt("assets/weighted_mouth_mask.txt").astype(np.float32).flatten() 46 | eye_mask = np.loadtxt("assets/weighted_eye_mask.txt", dtype=np.float32).flatten() 47 | eye_keypoints = np.loadtxt("assets/eye_keypoints.txt", dtype=np.float32).flatten() 48 | 49 | # define train and validation dataset 50 | train_dataset = DataReader() 51 | val_dataset = DataReader() 52 | 53 | # create models 54 | geom_unet = VertexUnet(classes=config["expression_space"]["classes"], 55 | heads=config["expression_space"]["heads"], 56 | n_vertices=train_dataset.n_vertices, 57 | mean=mean, 58 | stddev=stddev, 59 | ) 60 | print(f"geom_unet: {geom_unet.num_trainable_parameters()} trainable parameters") 61 | encoder = MultimodalEncoder(classes=config["expression_space"]["classes"], 62 | heads=config["expression_space"]["heads"], 63 | expression_dim=128, 64 | audio_dim=128, 65 | n_vertices=train_dataset.n_vertices, 66 | mean=mean, 67 | stddev=stddev 68 | ) 69 | print(f"encoder: {encoder.num_trainable_parameters()} trainable parameters") 70 | 71 | # train 72 | forwarder = DiscreteExpressionForwarder(config, geom_unet, encoder, mouth_mask, eye_mask, eye_keypoints) 73 | trainer = Trainer(config, forwarder, train_dataset, val_dataset) 74 | trainer.train() 75 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # meshtalk 2 | 3 | This repository contains code to run [MeshTalk](https://openaccess.thecvf.com/content/ICCV2021/papers/Richard_MeshTalk_3D_Face_Animation_From_Speech_Using_Cross-Modality_Disentanglement_ICCV_2021_paper.pdf) for face animation from audio. If you use MeshTalk, please cite 4 | ``` 5 | @inproceedings{richard2021meshtalk, 6 | author = {Richard, Alexander and Zollh\"ofer, Michael and Wen, Yandong and de la Torre, Fernando and Sheikh, Yaser}, 7 | title = {MeshTalk: 3D Face Animation From Speech Using Cross-Modality Disentanglement}, 8 | booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, 9 | month = {October}, 10 | year = {2021}, 11 | pages = {1173-1182} 12 | } 13 | ``` 14 | 15 | ## Supplemental Material 16 | [![Watch the video](https://github.com/facebookresearch/meshtalk/blob/main/supplemental_video.png)](https://www.facebook.com/MetaResearch/videos/251508987094387/) 17 | 18 | ## Running MeshTalk 19 | 20 | ### Dependencies 21 | 22 | ``` 23 | ffmpeg 24 | numpy 25 | torch (tested with v1.10.0) 26 | pytorch3d (tested with v0.4.0) 27 | torchaudio (tested with v0.10.0) 28 | ``` 29 | 30 | ### Animating a Face Mesh from Audio 31 | 32 | Download the [pretrained models](https://github.com/facebookresearch/meshtalk/releases/download/pretrained_models_v1.0/pretrained_models.zip) and unzip them. 33 | Make sure your python path contains the root directory (`export PYTHONPATH=`). 34 | 35 | Then, run 36 | ``` 37 | python animate_face.py --model_dir --audio_file --output 38 | ``` 39 | See a description of command line arguments via `python animate_face.py --help`. We provide a neutral face template mesh in `assets/face_template.obj`. Note that the rendered results look slightly different than in the paper and supplemental video because we use a differnt (open source) rendering engine in this repository. 40 | 41 | ### Training your own MeshTalk version 42 | 43 | All training code is available in the `training` directory. The training follows a two-step recipe. First, learn the latent expression code by running `train_step1.py`, second learn the autoregressive model by running `train_step2.py`. 44 | 45 | Note that we only provide a dataloader that produces dummy data which is always zero. For your training data, implement your own data reader that produces the same assets (template mesh, mesh sequence, audio sequence) as the dummy data reader. 46 | 47 | One potential dataset to use for MeshTalk is the [Multiface](https://github.com/facebookresearch/multiface) dataset which contains a subset (13 subjects) of the data used in the paper. The dataset includes tracked meshes and audio files. 48 | 49 | Note that the geometries in multiface have a slightly different topology than in meshtalk. To convert geometries from multiface to meshtalk, run 50 | ``` 51 | python utils/multiface2meshtalk.py 52 | ``` 53 | on the `.bin` files containing the vertex positions of the multiface meshes. Note that the input must be the `.bin` files from the `tracked_mesh` directories in multiface, **not** the `.obj` files. The output is a `.obj` file in the same format as `assets/face_template.obj`. 54 | 55 | ## License 56 | 57 | The code and dataset are released under [CC-NC 4.0 International license](https://github.com/facebookresearch/BinauralSpeechSynthesis/blob/main/LICENSE). 58 | 59 | -------------------------------------------------------------------------------- /CODE_OF_CONDUCT.md: -------------------------------------------------------------------------------- 1 | # Code of Conduct 2 | 3 | ## Our Pledge 4 | 5 | In the interest of fostering an open and welcoming environment, we as 6 | contributors and maintainers pledge to make participation in our project and 7 | our community a harassment-free experience for everyone, regardless of age, body 8 | size, disability, ethnicity, sex characteristics, gender identity and expression, 9 | level of experience, education, socio-economic status, nationality, personal 10 | appearance, race, religion, or sexual identity and orientation. 11 | 12 | ## Our Standards 13 | 14 | Examples of behavior that contributes to creating a positive environment 15 | include: 16 | 17 | * Using welcoming and inclusive language 18 | * Being respectful of differing viewpoints and experiences 19 | * Gracefully accepting constructive criticism 20 | * Focusing on what is best for the community 21 | * Showing empathy towards other community members 22 | 23 | Examples of unacceptable behavior by participants include: 24 | 25 | * The use of sexualized language or imagery and unwelcome sexual attention or 26 | advances 27 | * Trolling, insulting/derogatory comments, and personal or political attacks 28 | * Public or private harassment 29 | * Publishing others' private information, such as a physical or electronic 30 | address, without explicit permission 31 | * Other conduct which could reasonably be considered inappropriate in a 32 | professional setting 33 | 34 | ## Our Responsibilities 35 | 36 | Project maintainers are responsible for clarifying the standards of acceptable 37 | behavior and are expected to take appropriate and fair corrective action in 38 | response to any instances of unacceptable behavior. 39 | 40 | Project maintainers have the right and responsibility to remove, edit, or 41 | reject comments, commits, code, wiki edits, issues, and other contributions 42 | that are not aligned to this Code of Conduct, or to ban temporarily or 43 | permanently any contributor for other behaviors that they deem inappropriate, 44 | threatening, offensive, or harmful. 45 | 46 | ## Scope 47 | 48 | This Code of Conduct applies within all project spaces, and it also applies when 49 | an individual is representing the project or its community in public spaces. 50 | Examples of representing a project or community include using an official 51 | project e-mail address, posting via an official social media account, or acting 52 | as an appointed representative at an online or offline event. Representation of 53 | a project may be further defined and clarified by project maintainers. 54 | 55 | ## Enforcement 56 | 57 | Instances of abusive, harassing, or otherwise unacceptable behavior may be 58 | reported by contacting the project team at . All 59 | complaints will be reviewed and investigated and will result in a response that 60 | is deemed necessary and appropriate to the circumstances. The project team is 61 | obligated to maintain confidentiality with regard to the reporter of an incident. 62 | Further details of specific enforcement policies may be posted separately. 63 | 64 | Project maintainers who do not follow or enforce the Code of Conduct in good 65 | faith may face temporary or permanent repercussions as determined by other 66 | members of the project's leadership. 67 | 68 | ## Attribution 69 | 70 | This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, 71 | available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html 72 | 73 | [homepage]: https://www.contributor-covenant.org 74 | 75 | For answers to common questions about this code of conduct, see 76 | https://www.contributor-covenant.org/faq 77 | -------------------------------------------------------------------------------- /animate_face.py: -------------------------------------------------------------------------------- 1 | """ 2 | Copyright (c) Facebook, Inc. and its affiliates. 3 | All rights reserved. 4 | This source code is licensed under the license found in the 5 | LICENSE file in the root directory of this source tree. 6 | """ 7 | 8 | import argparse 9 | import numpy as np 10 | import torch as th 11 | 12 | from utils.renderer import Renderer 13 | from utils.helpers import smooth_geom, load_mask, get_template_verts, load_audio, audio_chunking 14 | from models.vertex_unet import VertexUnet 15 | from models.context_model import ContextModel 16 | from models.encoders import MultimodalEncoder 17 | 18 | 19 | parser = argparse.ArgumentParser() 20 | parser.add_argument("--model_dir", 21 | type=str, 22 | default="pretrained_models", 23 | help="directory containing the models to load") 24 | parser.add_argument("--audio_file", 25 | type=str, 26 | default="assets/example_sentence.wav", 27 | help="wave file to use for face animation" 28 | ) 29 | parser.add_argument("--face_template", 30 | type=str, 31 | default="assets/face_template.obj", 32 | help=".obj file containing neutral template mesh" 33 | ) 34 | parser.add_argument("--output", 35 | type=str, 36 | default="video.mp4", 37 | help="video output file" 38 | ) 39 | args = parser.parse_args() 40 | 41 | """ 42 | load assets 43 | """ 44 | print("load assets...") 45 | template_verts = get_template_verts(args.face_template) 46 | audio = load_audio(args.audio_file) 47 | mean = th.from_numpy(np.load("assets/face_mean.npy")) 48 | stddev = th.from_numpy(np.load("assets/face_std.npy")) 49 | forehead_mask = th.from_numpy(load_mask("assets/forehead_mask.txt", dtype=np.float32)).cuda() 50 | neck_mask = th.from_numpy(load_mask("assets/neck_mask.txt", dtype=np.float32)).cuda() 51 | 52 | renderer = Renderer("assets/face_template.obj") 53 | 54 | """ 55 | load models 56 | """ 57 | print("load models...") 58 | geom_unet = VertexUnet(classes=128, 59 | heads=16, 60 | n_vertices=6172, 61 | mean=mean, 62 | stddev=stddev, 63 | ) 64 | geom_unet.load(args.model_dir) 65 | geom_unet.cuda().eval() 66 | context_model = ContextModel(classes=128, 67 | heads=16, 68 | audio_dim=128 69 | ) 70 | context_model.load(args.model_dir) 71 | context_model.cuda().eval() 72 | encoder = MultimodalEncoder(classes=128, 73 | heads=16, 74 | expression_dim=128, 75 | audio_dim=128, 76 | n_vertices=6172, 77 | mean=mean, 78 | stddev=stddev, 79 | ) 80 | encoder.load(args.model_dir) 81 | encoder.cuda().eval() 82 | 83 | """ 84 | generate and render sequence 85 | """ 86 | print("animate face mesh...") 87 | # run template mesh and audio through networks 88 | audio = audio_chunking(audio, frame_rate=30, chunk_size=16000) 89 | with th.no_grad(): 90 | audio_enc = encoder.audio_encoder(audio.cuda().unsqueeze(0))["code"] 91 | one_hot = context_model.sample(audio_enc, argmax=False)["one_hot"] 92 | T = one_hot.shape[1] 93 | geom = template_verts.cuda().view(1, 1, 6172, 3).expand(-1, T, -1, -1).contiguous() 94 | result = geom_unet(geom, one_hot)["geom"].squeeze(0) 95 | # smooth results 96 | result = smooth_geom(result, forehead_mask) 97 | result = smooth_geom(result, neck_mask) 98 | # render sequence 99 | print("render...") 100 | renderer.to_video(result, args.audio_file, args.output) 101 | print("done") 102 | -------------------------------------------------------------------------------- /training/trainer.py: -------------------------------------------------------------------------------- 1 | """ 2 | Copyright (c) Facebook, Inc. and its affiliates. 3 | All rights reserved. 4 | This source code is licensed under the license found in the 5 | LICENSE file in the root directory of this source tree. 6 | """ 7 | 8 | import time 9 | import tqdm 10 | import torch as th 11 | import torch.optim as optim 12 | 13 | from torch.utils.data import DataLoader 14 | 15 | 16 | def cycle(dataloader): 17 | while True: 18 | for data in dataloader: 19 | yield data 20 | 21 | 22 | class Trainer: 23 | def __init__(self, config, forwarder, train_dataset, val_dataset): 24 | self.config = config 25 | self.forwarder = forwarder 26 | self.val_dataset = val_dataset 27 | self.train_dataloader = DataLoader(train_dataset, 28 | batch_size=self.config["train"]["batch_size"], 29 | shuffle=True, 30 | drop_last=True, 31 | num_workers=self.config["dataloader_workers"]) 32 | self.train_dataloader = cycle(self.train_dataloader) 33 | 34 | self.optimizer = {} 35 | for model_name, model in forwarder.models().items(): 36 | self.optimizer[model_name] = optim.Adam( 37 | filter(lambda x: x.requires_grad, model.parameters()), 38 | lr=self.config["train"]["learning_rate"], 39 | **(self.forwarder.train_args(model_name)) 40 | ) 41 | 42 | def save(self, suffix=""): 43 | for model in self.forwarder.models().values(): 44 | model.save(self.config["artifacts_dir"], suffix) 45 | 46 | def train(self): 47 | t = time.time() 48 | for iters in tqdm.tqdm(range(1, self.config["train"]["max_iterations"] + 1)): 49 | # training 50 | data = next(self.train_dataloader) 51 | loss_dict = self.training_step(data) 52 | 53 | # decrease learning rate if requested in config 54 | if iters in self.config["train"]["decrease_lr_iters"]: 55 | for opt in self.optimizer.values(): 56 | for param_group in opt.param_groups: 57 | param_group['lr'] = param_group['lr'] * 0.1 58 | 59 | if iters % 1000 == 0: 60 | # process random batch from validation set 61 | loss_val_dict = self.validation_step() 62 | # log some information about this iteration 63 | iter_str = "iter: {:6d}".format(iters) 64 | loss_str = ", ".join(["{}: {:.3f}".format(k, v.item()) for k, v in loss_dict.items()]) 65 | loss_val_str = ", ".join(["{}: {:.3f}".format(k, v.item()) for k, v in loss_val_dict.items()]) 66 | t = time.localtime(time.time() - t) 67 | duration = f"time: {t.tm_min:02d}:{t.tm_sec:02d}" 68 | print(", ".join([iter_str, loss_str, loss_val_str, duration])) 69 | t = time.time() 70 | 71 | # save model 72 | self.save() 73 | 74 | def training_step(self, data): 75 | # phase -> train 76 | for model in self.forwarder.models().values(): 77 | model.train() 78 | 79 | # train generator 80 | for opt in self.optimizer.values(): 81 | opt.zero_grad() 82 | 83 | loss_dict = self.forwarder.forward(data) 84 | loss = [loss_dict[k] * self.config["train"]["loss_weights"].get(k, 1.0) for k in self.config["train"]["loss_terms"]] 85 | loss = th.sum(th.stack(loss)) 86 | loss.backward() 87 | 88 | for opt in self.optimizer.values(): 89 | opt.step() 90 | 91 | return loss_dict 92 | 93 | def validation_step(self): 94 | # phase -> eval 95 | for model in self.forwarder.models().values(): 96 | model.eval() 97 | 98 | val_dataloader = DataLoader(self.val_dataset, 99 | batch_size=self.config["train"]["batch_size"], 100 | shuffle=True, 101 | drop_last=True, 102 | num_workers=1) 103 | val_dataloader = cycle(val_dataloader) 104 | data = next(val_dataloader) 105 | loss_dict = self.forwarder.forward(data) 106 | loss_dict = {k + "_val": v for k, v in loss_dict.items()} 107 | 108 | return loss_dict 109 | -------------------------------------------------------------------------------- /utils/renderer.py: -------------------------------------------------------------------------------- 1 | """ 2 | Copyright (c) Facebook, Inc. and its affiliates. 3 | All rights reserved. 4 | This source code is licensed under the license found in the 5 | LICENSE file in the root directory of this source tree. 6 | """ 7 | 8 | import ffmpeg 9 | import numpy as np 10 | import torch as th 11 | from pytorch3d.io import load_obj 12 | from pytorch3d.structures import Meshes 13 | from pytorch3d.renderer import ( 14 | look_at_view_transform, 15 | PerspectiveCameras, 16 | PointLights, 17 | RasterizationSettings, 18 | MeshRenderer, 19 | MeshRasterizer, 20 | SoftPhongShader, 21 | Textures, 22 | ) 23 | 24 | 25 | class Renderer: 26 | def __init__(self, face_topo_file: str): 27 | """ 28 | :param face_topo_file: .obj file containing face topology 29 | """ 30 | if th.cuda.is_available(): 31 | self.device = th.device("cuda:0") 32 | th.cuda.set_device(self.device) 33 | else: 34 | self.device = th.device("cpu") 35 | verts, faces_idx, _ = load_obj(face_topo_file) 36 | self.verts_rgb = th.ones_like(verts)[None] * th.Tensor([0.529, 0.807, 0.980])[None, None, :] # (1, V, 3) 37 | self.verts_rgb = self.verts_rgb.to(self.device) 38 | self.faces = faces_idx.verts_idx.to(self.device)[None, :, :] 39 | 40 | def render(self, verts: th.Tensor): 41 | """ 42 | :param verts: B x V x 3 tensor containing a batch of face vertex positions to be rendered 43 | :return: B x 640 x 480 x 4 tensor containing the rendered images 44 | """ 45 | R, T = look_at_view_transform(7.5, 0, 20) 46 | focal = th.tensor([5.0], dtype=th.float32).to(self.device) 47 | princpt = th.tensor([0.1, 0.1], dtype=th.float32).to(self.device).unsqueeze(0) 48 | 49 | cameras = PerspectiveCameras(device=self.device, focal_length=focal, R=R, T=T, principal_point=princpt) 50 | 51 | raster_settings = RasterizationSettings( 52 | image_size=[640, 480], 53 | blur_radius=0.0, 54 | faces_per_pixel=1, 55 | ) 56 | 57 | lights = PointLights(device=self.device, location=[[0.0, 0.0, 10.0]]) 58 | 59 | verts = verts * 0.01 60 | textures = Textures(verts_rgb=self.verts_rgb.expand(verts.shape[0], -1, -1)) 61 | mesh = Meshes( 62 | verts=verts.to(self.device), 63 | faces=self.faces.expand(verts.shape[0], -1, -1), 64 | textures=textures 65 | ) 66 | 67 | with th.no_grad(): 68 | renderer = MeshRenderer( 69 | rasterizer=MeshRasterizer( 70 | cameras=cameras, 71 | raster_settings=raster_settings 72 | ), 73 | shader=SoftPhongShader( 74 | device=self.device, 75 | cameras=cameras, 76 | lights=lights 77 | ) 78 | ) 79 | images = renderer(mesh) 80 | 81 | return images 82 | 83 | def to_video(self, verts: th.Tensor, audio_file: str, video_output: str, fps: int = 30, batch_size: int = 30): 84 | """ 85 | :param verts: B x V x 3 tensor containing a batch of face vertex positions to be rendered 86 | :param audio_file: filename of the audio input file 87 | :param video_output: filename of the output video file 88 | :param fps: frame rate of output video 89 | :param batch_size: number of frames to render simultaneously in one batch 90 | """ 91 | if not video_output[-4:] == '.mp4': 92 | video_output = video_output + '.mp4' 93 | 94 | images = th.cat([self.render(v).cpu() for v in th.split(verts, batch_size)], dim=0) 95 | images = 255 * images[:, :, :, :3].contiguous().numpy() 96 | images = images.astype(np.uint8) 97 | 98 | video_stream = ffmpeg.input("pipe:", format="rawvideo", pix_fmt="rgb24", s="480x640", r=fps) 99 | audio_stream = ffmpeg.input(filename=audio_file) 100 | streams = [video_stream, audio_stream] 101 | output_args = { 102 | "format": "mp4", 103 | "pix_fmt": "yuv420p", 104 | "vcodec": "libx264", 105 | "movflags": "frag_keyframe+empty_moov+faststart" 106 | } 107 | proc = ( 108 | ffmpeg 109 | .output(*streams, video_output, **output_args) 110 | .overwrite_output() 111 | .global_args("-loglevel", "fatal") 112 | .run_async(pipe_stdin=True, pipe_stdout=False) 113 | ) 114 | 115 | proc.communicate(input=images.tobytes()) 116 | -------------------------------------------------------------------------------- /models/vertex_unet.py: -------------------------------------------------------------------------------- 1 | """ 2 | Copyright (c) Facebook, Inc. and its affiliates. 3 | All rights reserved. 4 | This source code is licensed under the license found in the 5 | LICENSE file in the root directory of this source tree. 6 | """ 7 | 8 | import torch as th 9 | import torch.nn.functional as F 10 | 11 | from utils.helpers import Net 12 | 13 | 14 | class VertexUnet(Net): 15 | def __init__(self, classes: int = 128, heads: int = 64, n_vertices: int = 6172, mean: th.Tensor = None, 16 | stddev: th.Tensor = None, model_name: str = 'vertex_unet'): 17 | """ 18 | VertexUnet consumes a neutral template mesh and an expression encoding and produces an animated face mesh 19 | :param classes: number of classes for the categorical latent embedding 20 | :param heads: number of heads for the categorical latent embedding 21 | :param n_vertices: number of vertices in the face mesh 22 | :param mean: mean position of each vertex 23 | :param stddev: standard deviation of each vertex position 24 | :param model_name: name of the model, used to load and save the model 25 | """ 26 | super().__init__(model_name) 27 | 28 | self.classes = classes 29 | self.heads = heads 30 | self.n_vertices = n_vertices 31 | 32 | shape = (1, 1, n_vertices, 3) 33 | self.register_buffer("mean", th.zeros(shape) if mean is None else mean.view(shape)) 34 | self.register_buffer("stddev", th.ones(shape) if stddev is None else stddev.view(shape)) 35 | 36 | # encoder layers 37 | self.encoder = th.nn.ModuleList([ 38 | th.nn.Linear(n_vertices*3, 512), 39 | th.nn.Linear(512, 256), 40 | th.nn.Linear(256, 128) 41 | ]) 42 | 43 | # multimodal fusion 44 | self.fusion = th.nn.Linear(heads * classes + 128, 128) 45 | 46 | # decoder layers 47 | self.temporal = th.nn.LSTM(input_size=128, hidden_size=128, num_layers=2, batch_first=True) 48 | self.decoder = th.nn.ModuleList([ 49 | th.nn.Linear(128, 256), 50 | th.nn.Linear(256, 512), 51 | th.nn.Linear(512, n_vertices*3) 52 | ]) 53 | self.vertex_bias = th.nn.Parameter(th.zeros(n_vertices * 3)) 54 | 55 | def _encode(self, x: th.Tensor): 56 | """ 57 | encode the neutral face mesh through the UNet 58 | :param x: B x T x n_vertices * 3 Tensor containing vertices of neutral face mesh 59 | :return: x: B x T x 128 Tensor containing a 128-dim encoding of each input mesh 60 | skips: outputs after each of the UNet encoder layers 61 | """ 62 | skips = [] 63 | for i, layer in enumerate(self.encoder): 64 | skips = [x] + skips 65 | x = F.leaky_relu(layer(x), 0.2) 66 | return x, skips 67 | 68 | def _fuse(self, geom_encoding: th.Tensor, expression_encoding: th.Tensor): 69 | """ 70 | :param geom_encoding: B x T x 128 Tensor containing the encoding of the neutral face meshes 71 | :param expression_encoding: B x T x heads x classes Tensor containing the one hot expression encodings 72 | :return: B x T x 128 Tensor containing a latent representation of the animated face 73 | """ 74 | expression_encoding = expression_encoding.view( 75 | expression_encoding.shape[0], expression_encoding.shape[1], self.heads * self.classes 76 | ) 77 | x = self.fusion(th.cat([geom_encoding, expression_encoding], dim=-1)) 78 | x = F.leaky_relu(x, 0.2) 79 | return x 80 | 81 | def _decode(self, x: th.Tensor, skips: th.Tensor): 82 | """ 83 | :param x: B x T x 128 Tensor containing a latent representation of the animated face 84 | :param skips: outputs of each UNet encoder layer to be added back to the decoder layers 85 | :return: B x T x n_vertices * 3 Tensor containing the animated face meshes 86 | """ 87 | x, _ = self.temporal(x) 88 | for i, layer in enumerate(self.decoder): 89 | x = skips[i] + F.leaky_relu(layer(x), 0.2) 90 | x = x + self.vertex_bias.view(1, 1, -1) 91 | return x 92 | 93 | def forward(self, geom: th.Tensor, expression_encoding: th.Tensor): 94 | """ 95 | :param geom: B x T x n_vertices x 3 Tensor containing template face meshes 96 | :param expression_encoding: B x T x heads x classes Tensor containing one hot expression encodings 97 | :return: geom: B x T x n_vertices x 3 Tensor containing predicted face meshes 98 | """ 99 | x = (geom - self.mean) / self.stddev 100 | x = x.view(x.shape[0], x.shape[1], self.n_vertices*3) 101 | 102 | geom_encoding, skips = self._encode(x) 103 | x = self._fuse(geom_encoding, expression_encoding) 104 | x = self._decode(x, skips) 105 | 106 | x = x.view(x.shape[0], x.shape[1], self.n_vertices, 3) 107 | geom = x * self.stddev + self.mean 108 | return {"geom": geom} 109 | -------------------------------------------------------------------------------- /utils/helpers.py: -------------------------------------------------------------------------------- 1 | """ 2 | Copyright (c) Facebook, Inc. and its affiliates. 3 | All rights reserved. 4 | This source code is licensed under the license found in the 5 | LICENSE file in the root directory of this source tree. 6 | """ 7 | 8 | import numpy as np 9 | import torch as th 10 | import torchaudio as ta 11 | from pytorch3d.io import load_obj 12 | 13 | 14 | def load_mask(mask_file: str, dtype = bool): 15 | """ 16 | :param mask_file: filename of mask to load 17 | :param dtype: python type, bool for binary masks, np.float32 for float masks 18 | :return: np.array containing the loaded mask of type dtype 19 | """ 20 | return np.loadtxt(mask_file).astype(dtype).flatten() 21 | 22 | 23 | def load_audio(wave_file: str): 24 | """ 25 | :param wave_file: .wav file containing the audio input 26 | :return: 1 x T tensor containing input audio resampled to 16kHz 27 | """ 28 | audio, sr = ta.load(wave_file) 29 | if not sr == 16000: 30 | audio = ta.transforms.Resample(sr, 16000)(audio) 31 | if audio.shape[0] > 1: 32 | audio = th.mean(audio, dim=0, keepdim=True) 33 | # normalize such that energy matches average energy of audio used in training 34 | audio = 0.01 * audio / th.mean(th.abs(audio)) 35 | return audio 36 | 37 | 38 | def audio_chunking(audio: th.Tensor, frame_rate: int = 30, chunk_size: int = 16000): 39 | """ 40 | :param audio: 1 x T tensor containing a 16kHz audio signal 41 | :param frame_rate: frame rate for video (we need one audio chunk per video frame) 42 | :param chunk_size: number of audio samples per chunk 43 | :return: num_chunks x chunk_size tensor containing sliced audio 44 | """ 45 | samples_per_frame = 16000 // frame_rate 46 | padding = (chunk_size - samples_per_frame) // 2 47 | audio = th.nn.functional.pad(audio.unsqueeze(0), pad=[padding, padding]).squeeze(0) 48 | anchor_points = list(range(chunk_size//2, audio.shape[-1]-chunk_size//2, samples_per_frame)) 49 | audio = th.cat([audio[:, i-chunk_size//2:i+chunk_size//2] for i in anchor_points], dim=0) 50 | return audio 51 | 52 | 53 | def smooth_geom(geom, mask: th.Tensor = None, filter_size: int = 9, sigma: float = 2.0): 54 | """ 55 | :param geom: T x V x 3 tensor containing a temporal sequence of length T with V vertices in each frame 56 | :param mask: V-dimensional Tensor containing a mask with vertices to be smoothed 57 | :param filter_size: size of the Gaussian filter 58 | :param sigma: standard deviation of the Gaussian filter 59 | :return: T x V x 3 tensor containing smoothed geometry (i.e., smoothed in the area indicated by the mask) 60 | """ 61 | assert filter_size % 2 == 1, f"filter size must be odd but is {filter_size}" 62 | # Gaussian smoothing (low-pass filtering) 63 | fltr = np.arange(-(filter_size // 2), filter_size // 2 + 1) 64 | fltr = np.exp(-0.5 * fltr ** 2 / sigma ** 2) 65 | fltr = th.Tensor(fltr) / np.sum(fltr) 66 | # apply fltr 67 | fltr = fltr.view(1, 1, -1).to(device=geom.device) 68 | T, V = geom.shape[0], geom.shape[1] 69 | g = th.nn.functional.pad( 70 | geom.permute(1, 2, 0).view(V * 3, 1, T), 71 | pad=[filter_size // 2, filter_size // 2], mode='replicate' 72 | ) 73 | g = th.nn.functional.conv1d(g, fltr).view(V, 3, T) 74 | smoothed = g.permute(2, 0, 1).contiguous() 75 | # blend smoothed signal with original signal 76 | if mask is None: 77 | return smoothed 78 | else: 79 | return smoothed * mask[None, :, None] + geom * (-mask[None, :, None] + 1) 80 | 81 | 82 | def get_template_verts(template_mesh: str): 83 | """ 84 | :param template_mesh: .obj file containing the neutral face template mesh 85 | :return: V x 3 tensor containing the template vertices 86 | """ 87 | verts, _, _ = load_obj(template_mesh) 88 | return verts 89 | 90 | 91 | class Net(th.nn.Module): 92 | 93 | def __init__(self, model_name: str = "network"): 94 | """ 95 | :param model_name: name of the model 96 | """ 97 | super().__init__() 98 | self.model_name = model_name 99 | 100 | def save(self, model_dir: str, suffix: str = ''): 101 | """ 102 | :param model_dir: directory where the model should be stored 103 | :param suffix: option suffix to append to the network name 104 | """ 105 | self.cpu() 106 | if suffix == "": 107 | fname = f"{model_dir}/{self.model_name}.pkl" 108 | else: 109 | fname = f"{model_dir}/{self.model_name}.{suffix}.pkl" 110 | th.save(self.state_dict(), fname) 111 | self.cuda() 112 | return self 113 | 114 | def load(self, model_dir: str, suffix: str = ''): 115 | """ 116 | :param model_dir: directory where the model is be stored 117 | :param suffix: optional suffix to append to the network name 118 | """ 119 | self.cpu() 120 | if suffix == "": 121 | fname = f"{model_dir}/{self.model_name}.pkl" 122 | else: 123 | fname = f"{model_dir}/{self.model_name}.{suffix}.pkl" 124 | states = th.load(fname) 125 | self.load_state_dict(states) 126 | self.cuda() 127 | print("Loaded:", fname) 128 | return self 129 | 130 | def num_trainable_parameters(self): 131 | """ 132 | :return: number of trainable parameters in the model 133 | """ 134 | return sum(p.numel() for p in self.parameters() if p.requires_grad) 135 | -------------------------------------------------------------------------------- /training/forwarder.py: -------------------------------------------------------------------------------- 1 | """ 2 | Copyright (c) Facebook, Inc. and its affiliates. 3 | All rights reserved. 4 | This source code is licensed under the license found in the 5 | LICENSE file in the root directory of this source tree. 6 | """ 7 | 8 | import random 9 | import torch as th 10 | 11 | 12 | def _gumbel_softmax(logprobs, tau=1.0, argmax=False): 13 | if argmax: 14 | logits = logprobs / tau 15 | else: 16 | g = -th.log(-th.log(th.clamp(th.rand(logprobs.shape, device=logprobs.device), min=1e-10, max=1))) 17 | logits = (g + logprobs) / tau 18 | soft_labels = th.softmax(logits, dim=-1) 19 | labels = soft_labels.detach().argmax(dim=-1, keepdim=True) 20 | hard_labels = th.zeros(logits.shape, device=logits.device) 21 | hard_labels = hard_labels.scatter(-1, labels, 1.0) 22 | one_hot = hard_labels.detach() - soft_labels.detach() + soft_labels 23 | return one_hot, labels.squeeze(-1) 24 | 25 | 26 | def quantize(logprobs, argmax=False): 27 | """ 28 | :param logprobs: B x T x heads x classes 29 | argmax: use argmax instead of sampling gumble noise if True 30 | :return: one_hot: B x T x heads x classes, where each class column is a one hot vector 31 | labels: B x T x heads (LongTensor), where each element is a class label 32 | """ 33 | one_hot, labels = _gumbel_softmax(logprobs, argmax=argmax) 34 | return { 35 | "one_hot": one_hot, 36 | "labels": labels 37 | } 38 | 39 | 40 | class DiscreteExpressionForwarder: 41 | """ this is the class to train the expression codes (train_step1.py) """ 42 | def __init__(self, config, geom_unet, encoder, mouth_mask, eye_mask, landmarks): 43 | self.config = config 44 | self.mouth_mask = th.from_numpy(mouth_mask).type(th.float32).cuda() 45 | self.eye_mask = th.from_numpy(eye_mask).type(th.float32).cuda() 46 | self.landmarks = th.from_numpy(landmarks).type(th.float32).cuda() 47 | self.geom_unet = geom_unet.cuda() 48 | self.encoder = encoder.cuda() 49 | 50 | def models(self): 51 | return { 52 | "geom_unet": self.geom_unet, 53 | "encoder": self.encoder, 54 | } 55 | 56 | def train_args(self, model_name): 57 | if model_name == "geom_unet": 58 | return {"weight_decay": self.config["train"]["weight_decay"]} 59 | else: 60 | return {} 61 | 62 | def load(self, model_dir, suffix=""): 63 | self.geom_unet.load(model_dir, suffix) 64 | self.encoder.load(model_dir, suffix) 65 | 66 | def random_shift(self, size): 67 | return (th.arange(size) + random.randint(1, size-1)) % size 68 | 69 | def _reconstruct(self, template_geom, expression_code, audio_code): 70 | logprobs = self.encoder.fuse(expression_code, audio_code)["logprobs"] 71 | z = quantize(logprobs)["one_hot"] 72 | recon = self.geom_unet( 73 | template_geom.unsqueeze(1).expand(-1, z.shape[1], -1, -1).contiguous(), z 74 | )["geom"] 75 | return recon 76 | 77 | def forward(self, data): 78 | template = data["template"].cuda() 79 | geom = data["geom"].cuda() 80 | audio = data["audio"].cuda() 81 | B, T = geom.shape[0], geom.shape[1] 82 | loss_dict = {} 83 | 84 | enc = self.encoder.encode(geom, audio) 85 | recon = self._reconstruct(template, enc["expression_code"], enc["audio_code"]) 86 | 87 | # compute losses 88 | if "recon" in self.config["train"]["loss_terms"]: 89 | l2_loss = th.mean((recon - geom) ** 2) 90 | loss_dict.update({"recon": l2_loss}) 91 | 92 | if "landmarks" in self.config["train"]["loss_terms"]: 93 | lmk_loss = th.sum(((recon - geom) ** 2) * self.landmarks[None, None, :, None]) / \ 94 | (B * T * th.sum(self.landmarks) * 3) 95 | loss_dict.update({"landmarks": lmk_loss}) 96 | 97 | if "modality_crossing" in self.config["train"]["loss_terms"]: 98 | # keep audio, switch expression 99 | cross_recon = self._reconstruct(template, 100 | enc["expression_code"][self.random_shift(B), :, :], 101 | enc["audio_code"] 102 | ) 103 | audio_consistency_loss = th.sum(((cross_recon - geom) ** 2) * self.mouth_mask[None, None, :, None]) / \ 104 | (B * T * th.sum(self.mouth_mask) * 3) 105 | # keep expression, switch audio 106 | cross_recon = self._reconstruct(template, 107 | enc["expression_code"], 108 | enc["audio_code"][self.random_shift(B), :, :] 109 | ) 110 | expression_consistency_loss = th.sum(((cross_recon - geom) ** 2) * self.eye_mask[None, None, :, None]) / \ 111 | (B * T * th.sum(self.eye_mask) * 3) 112 | # add modality crossing loss to loss_dict 113 | loss_dict.update({"modality_crossing": audio_consistency_loss + expression_consistency_loss}) 114 | 115 | return loss_dict 116 | 117 | 118 | class CategoricalAutoregressiveForwarder: 119 | """ this is the class to train the autoregressive model (train_step2.py) """ 120 | def __init__(self, config, encoder, context_model): 121 | self.config = config 122 | self.context_model = context_model.cuda() 123 | # the encoder is a pre-trained model and is expected to be passed with loaded weights 124 | # the encoder is also hidden from the trainer 125 | self.encoder = encoder.cuda().eval() 126 | 127 | def models(self): 128 | return { 129 | "context_model": self.context_model 130 | } 131 | 132 | def train_args(self, model_name): 133 | return {} 134 | 135 | def load(self, model_dir, suffix=""): 136 | self.context_model.load(model_dir, suffix) 137 | 138 | def forward(self, data): 139 | geom = data["geom"].cuda() 140 | audio = data["audio"].cuda() 141 | 142 | enc = self.encoder(geom, audio) 143 | quantized = quantize(enc["logprobs"], argmax=True) 144 | one_hot = quantized["one_hot"].contiguous().detach() 145 | target_labels = quantized["labels"].contiguous().detach() 146 | 147 | logprobs = self.context_model(one_hot, enc["audio_code"])["logprobs"] 148 | 149 | loss_dict = {} 150 | 151 | if "cross_entropy" in self.config["train"]["loss_terms"]: 152 | ce_loss = th.nn.functional.nll_loss(logprobs.view(-1, logprobs.shape[-1]), target_labels.view(-1)) 153 | loss_dict.update({"cross_entropy": ce_loss}) 154 | 155 | return loss_dict 156 | -------------------------------------------------------------------------------- /utils/multiface2meshtalk.py: -------------------------------------------------------------------------------- 1 | 2 | """ 3 | Copyright (c) Facebook, Inc. and its affiliates. 4 | All rights reserved. 5 | This source code is licensed under the license found in the 6 | LICENSE file in the root directory of this source tree. 7 | """ 8 | 9 | import argparse 10 | import numpy as np 11 | 12 | 13 | ### vertex indices to remove from multiface topology ### 14 | ignore_vertices = {551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1156, 1157, 1158, 1159, 1160, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1299, 1514, 1657, 1658, 1659, 1660, 1662, 1663, 1664, 2010, 2231, 2232, 2233, 2234, 2235, 2236, 2237, 2238, 2239, 2240, 2241, 2242, 2243, 2244, 2245, 2468, 2469, 2470, 2471, 2472, 2473, 2474, 2475, 2476, 2477, 2478, 2479, 2480, 2481, 2482, 2483, 2484, 2485, 2486, 2487, 2488, 2489, 2490, 2491, 2492, 3102, 3103, 3104, 3105, 3106, 3107, 3108, 3109, 3163, 3164, 3165, 3166, 3167, 3169, 3170, 3171, 3172, 3173, 3174, 3176, 3177, 3178, 3179, 3200, 3205, 3206, 3207, 3246, 3247, 3248, 3249, 3250, 3251, 3252, 3253, 3254, 3255, 3256, 3257, 3258, 3259, 3260, 3261, 3262, 3263, 3264, 3265, 3266, 3267, 3268, 3269, 3270, 3271, 3272, 3273, 3274, 3275, 3276, 3277, 3278, 3279, 3280, 3281, 3282, 3283, 3284, 3285, 3286, 3287, 3288, 3289, 3290, 3291, 3292, 3293, 3294, 3295, 3296, 3297, 3298, 3299, 3300, 3301, 3302, 3303, 3304, 3305, 3306, 3307, 3308, 3309, 3310, 3311, 3312, 3313, 3314, 3315, 3316, 3317, 3318, 3319, 3320, 3321, 3322, 3323, 3324, 3325, 3326, 3327, 3328, 3329, 3330, 3331, 3332, 3333, 3334, 3335, 3336, 3337, 3339, 3343, 3351, 3355, 3356, 3364, 3365, 3374, 3384, 3385, 3386, 3387, 3388, 3389, 3418, 3419, 3420, 3422, 3423, 3424, 3425, 3426, 3427, 3429, 3430, 3431, 3432, 3433, 3434, 3435, 3436, 3437, 3438, 3439, 3440, 3441, 3442, 3443, 3444, 3447, 3448, 3449, 3450, 3451, 3452, 3453, 3454, 3455, 3469, 3470, 3471, 3472, 3473, 3474, 3475, 3476, 3477, 3478, 3479, 3480, 3481, 3482, 3483, 3484, 3485, 3486, 3487, 3488, 3489, 3490, 3491, 3492, 3493, 3494, 3495, 3496, 3497, 3498, 3499, 3500, 3501, 3502, 3503, 3504, 3505, 4651, 4652, 4653, 4654, 4655, 4656, 4657, 4658, 4659, 4660, 4661, 4662, 4663, 4664, 4665, 4666, 4667, 4668, 4669, 4670, 4671, 4672, 4673, 4674, 4675, 4676, 4677, 4678, 4679, 4680, 4681, 4682, 4683, 4684, 4685, 4686, 4687, 4688, 4689, 4690, 4691, 4692, 4693, 4694, 4695, 4696, 4697, 4698, 4699, 4700, 4701, 4702, 4703, 4704, 4705, 4706, 4707, 4708, 4709, 4710, 4711, 4712, 4713, 4714, 4715, 4716, 4717, 4718, 4719, 4720, 4721, 4722, 4723, 4724, 4725, 4726, 4727, 4728, 4729, 4730, 4731, 4732, 4733, 4734, 4735, 4736, 4737, 4738, 4739, 4740, 4741, 4742, 4743, 4744, 4745, 4746, 4747, 4748, 4749, 4750, 4751, 4752, 4753, 4754, 4755, 4756, 4757, 4758, 4759, 4760, 4761, 4762, 4763, 4764, 4765, 4766, 4767, 4768, 4769, 4770, 4771, 4772, 4773, 4774, 4775, 4776, 4777, 4778, 4779, 4780, 4781, 4782, 4783, 4784, 4785, 4786, 4787, 4788, 4789, 4790, 4791, 4792, 4793, 4794, 4795, 4796, 4797, 4798, 4799, 4800, 4801, 4802, 4803, 4804, 4805, 4806, 4807, 4808, 4809, 4810, 4811, 4812, 4813, 4814, 4815, 4816, 4817, 4818, 4819, 4820, 4821, 4822, 4824, 4826, 4827, 4828, 4829, 4830, 4831, 4832, 4833, 4834, 4835, 4836, 4837, 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5084, 5085, 5086, 5087, 5088, 5089, 5090, 5091, 5092, 5093, 5094, 5095, 5096, 5097, 5098, 5099, 5100, 5101, 5102, 5103, 5104, 5105, 5106, 5107, 5108, 5109, 5110, 5111, 5112, 5113, 5114, 5115, 5116, 5117, 5118, 5119, 5120, 5121, 5122, 5123, 5124, 5125, 5126, 5127, 5128, 5129, 5130, 5131, 5132, 5133, 5134, 5135, 5136, 5137, 5138, 5139, 5140, 5141, 5142, 5143, 5144, 5145, 5146, 5149, 5150, 5153, 5154, 5155, 5156, 5159, 5162, 5163, 5166, 5167, 5170, 5187, 5188, 5189, 5190, 5191, 5192, 5193, 5194, 5195, 5196, 5197, 5198, 5199, 5200, 5201, 5202, 5203, 5204, 5205, 5206, 5207, 5208, 5209, 5210, 5211, 5212, 5213, 5214, 5215, 5216, 5217, 5218, 5219, 5220, 5221, 5222, 5223, 5224, 5225, 5226, 5227, 5228, 5229, 5230, 5231, 5232, 5233, 5235, 5265, 5266, 5267, 5268, 5269, 5270, 5272, 5274, 5276, 5537, 5538, 5582, 5583, 5588, 6439, 6440, 6441, 6442, 6443, 6958, 6959, 6960, 6977, 6978, 6980, 6981, 6982, 6983, 6984, 6985, 6986, 6987, 6988, 6989, 6990, 6991, 6992, 6993, 6994, 6995, 6996, 6997, 6998, 6999, 7000, 7001, 7002, 7003, 7004, 7005, 7006, 7007, 7008, 7009, 7010, 7011} 15 | 16 | 17 | parser = argparse.ArgumentParser() 18 | parser.add_argument("multiface_geometry", type=str, help="Input: .bin file containing the geometry of a multiface mesh") 19 | parser.add_argument("meshtalk_geometry", type=str, help="Output: .obj file containing the topology of a meshtalk mesh (vertices + faces)") 20 | parser.add_argument("--template_topology", type=str, default="assets/face_template.obj") 21 | args = parser.parse_args() 22 | 23 | # load input .bin file from multiface 24 | if not args.multiface_geometry[-4:] == ".bin": 25 | raise Exception("Multiface geometry must be a .bin file") 26 | geom = np.fromfile(args.multiface_geometry, dtype=np.float32).reshape(-1, 3) 27 | valid_verts = [i for i in range(7306) if not i in ignore_vertices] 28 | geom = geom[valid_verts, :] 29 | geom = [f"v {v[0]:.7e} {v[1]:.7e} {v[2]:.7e}" for v in geom] 30 | 31 | # load meshtalk template topology 32 | if not args.template_topology[-4:] == ".obj": 33 | raise Exception("Meshtalk template topology must be a .obj file") 34 | with open(args.template_topology, "r") as f: 35 | topology = f.readlines() 36 | topology = [line.strip() for line in topology] 37 | f.close() 38 | 39 | topology = geom + topology[len(geom):] 40 | 41 | # save result to .obj file 42 | if not args.meshtalk_geometry[-4:] == ".obj": 43 | raise Exception("Output file must be a .obj file") 44 | with open(args.meshtalk_geometry, "w") as f: 45 | topology = [f"{line}\n" for line in topology] 46 | f.writelines(topology) 47 | f.close() 48 | 49 | -------------------------------------------------------------------------------- /models/encoders.py: -------------------------------------------------------------------------------- 1 | """ 2 | Copyright (c) Facebook, Inc. and its affiliates. 3 | All rights reserved. 4 | This source code is licensed under the license found in the 5 | LICENSE file in the root directory of this source tree. 6 | """ 7 | 8 | import torch as th 9 | import torch.nn.functional as F 10 | import torchaudio as ta 11 | 12 | from utils.helpers import Net 13 | 14 | 15 | class AudioEncoder(Net): 16 | def __init__(self, latent_dim: int = 128, model_name: str = 'audio_encoder'): 17 | """ 18 | :param latent_dim: size of the latent audio embedding 19 | :param model_name: name of the model, used to load and save the model 20 | """ 21 | super().__init__(model_name) 22 | 23 | self.melspec = ta.transforms.MelSpectrogram( 24 | sample_rate=16000, n_fft=2048, win_length=800, hop_length=160, n_mels=80 25 | ) 26 | 27 | conv_len = 5 28 | self.convert_dimensions = th.nn.Conv1d(80, 128, kernel_size=conv_len) 29 | self.weights_init(self.convert_dimensions) 30 | self.receptive_field = conv_len 31 | 32 | convs = [] 33 | for i in range(6): 34 | dilation = 2 * (i % 3 + 1) 35 | self.receptive_field += (conv_len - 1) * dilation 36 | convs += [th.nn.Conv1d(128, 128, kernel_size=conv_len, dilation=dilation)] 37 | self.weights_init(convs[-1]) 38 | self.convs = th.nn.ModuleList(convs) 39 | self.code = th.nn.Linear(128, latent_dim) 40 | 41 | self.apply(lambda x: self.weights_init(x)) 42 | 43 | def weights_init(self, m): 44 | if isinstance(m, th.nn.Conv1d): 45 | th.nn.init.xavier_uniform_(m.weight) 46 | try: 47 | th.nn.init.constant_(m.bias, .01) 48 | except: 49 | pass 50 | 51 | def forward(self, audio: th.Tensor): 52 | """ 53 | :param audio: B x T x 16000 Tensor containing 1 sec of audio centered around the current time frame 54 | :return: code: B x T x latent_dim Tensor containing a latent audio code/embedding 55 | """ 56 | B, T = audio.shape[0], audio.shape[1] 57 | x = self.melspec(audio).squeeze(1) 58 | x = th.log(x.clamp(min=1e-10, max=None)) 59 | if T == 1: 60 | x = x.unsqueeze(1) 61 | 62 | # Convert to the right dimensionality 63 | x = x.view(-1, x.shape[2], x.shape[3]) 64 | x = F.leaky_relu(self.convert_dimensions(x), .2) 65 | 66 | # Process stacks 67 | for conv in self.convs: 68 | x_ = F.leaky_relu(conv(x), .2) 69 | if self.training: 70 | x_ = F.dropout(x_, .2) 71 | l = (x.shape[2] - x_.shape[2]) // 2 72 | x = (x[:, :, l:-l] + x_) / 2 73 | 74 | x = th.mean(x, dim=-1) 75 | x = x.view(B, T, x.shape[-1]) 76 | x = self.code(x) 77 | 78 | return {"code": x} 79 | 80 | 81 | class ExpressionEncoder(Net): 82 | def __init__(self, latent_dim: int = 128, n_vertices: int = 6172, mean: th.Tensor = None, stddev: th.Tensor = None, 83 | model_name: str = 'expression_encoder'): 84 | """ 85 | :param latent_dim: size of the latent expression embedding before quantization through Gumbel softmax 86 | :param n_vertices: number of face mesh vertices 87 | :param mean: mean position of each vertex 88 | :param stddev: standard deviation of each vertex position 89 | :param model_name: name of the model, used to load and save the model 90 | """ 91 | super().__init__(model_name) 92 | 93 | self.n_vertices = n_vertices 94 | 95 | shape = (1, 1, n_vertices, 3) 96 | self.register_buffer("mean", th.zeros(shape) if mean is None else mean.view(shape)) 97 | self.register_buffer("stddev", th.ones(shape) if stddev is None else stddev.view(shape)) 98 | 99 | self.layers = th.nn.ModuleList([ 100 | th.nn.Linear(self.n_vertices * 3, 256), 101 | th.nn.Linear(256, 128), 102 | ]) 103 | self.lstm = th.nn.LSTM(input_size=128, hidden_size=128, num_layers=1, batch_first=True) 104 | self.code = th.nn.Linear(128, latent_dim) 105 | 106 | def forward(self, geom: th.Tensor): 107 | """ 108 | :param geom: B x T x n_vertices x 3 Tensor containing face geometries 109 | :return: code: B x T x heads x classes Tensor containing a latent expression code/embedding 110 | """ 111 | x = (geom - self.mean) / self.stddev 112 | x = x.view(x.shape[0], x.shape[1], self.n_vertices*3) 113 | 114 | for layer in self.layers: 115 | x = F.leaky_relu(layer(x), 0.2) 116 | x, _ = self.lstm(x) 117 | x = self.code(x) 118 | 119 | return {"code": x} 120 | 121 | 122 | class FusionMlp(Net): 123 | def __init__(self, classes: int = 128, heads: int = 64, expression_dim: int = 128, audio_dim: int = 128, 124 | model_name: str = 'fusion_model'): 125 | """ 126 | :param classes: number of classes for the categorical latent embedding 127 | :param heads: number of heads for the categorical latent embedding 128 | :param expression_dim: size of the latent expression embedding before quantization through Gumbel softmax 129 | :param audio_dim: size of the latent audio embedding 130 | :param model_name: name of the model, used to load and save the model 131 | """ 132 | super().__init__(model_name) 133 | self.classes = classes 134 | self.heads = heads 135 | 136 | latent_dim = 256 137 | self.mlp = th.nn.Sequential( 138 | th.nn.Linear(expression_dim + audio_dim, latent_dim), 139 | th.nn.LeakyReLU(negative_slope=0.2, inplace=True), 140 | th.nn.Linear(latent_dim, latent_dim), 141 | th.nn.LeakyReLU(negative_slope=0.2, inplace=True), 142 | th.nn.Linear(latent_dim, heads * classes) 143 | ) 144 | 145 | def forward(self, expression_code: th.Tensor, audio_code: th.Tensor): 146 | """ 147 | :param expression_code: B x T x expression_dim Tensor containing the expression encodings 148 | :param audio_code: B x T x audio_dim Tensor containing the audio encodings 149 | :return: logprobs: B x T x heads x classes Tensor containing logprobs for each categorical head 150 | """ 151 | x = th.cat([expression_code, audio_code], dim=-1) 152 | x = self.mlp(x).view(x.shape[0], x.shape[1], self.heads, self.classes) 153 | logprobs = F.log_softmax(x, dim=-1) 154 | return {"logprobs": logprobs} 155 | 156 | 157 | class MultimodalEncoder(Net): 158 | def __init__(self, 159 | classes: int = 128, 160 | heads: int = 64, 161 | expression_dim: int = 128, 162 | audio_dim: int = 128, 163 | n_vertices: int = 6172, 164 | mean: th.Tensor = None, 165 | stddev: th.Tensor = None, 166 | model_name: str = "encoder" 167 | ): 168 | """ 169 | :param classes: number of classes for the categorical latent embedding 170 | :param heads: number of heads for the categorical latent embedding 171 | :param expression_dim: size of the latent expression embedding before quantization through Gumbel softmax 172 | :param audio_dim: size of the latent audio embedding 173 | :param n_vertices: number of vertices in the face mesh 174 | :param mean: mean position of each vertex 175 | :param stddev: standard deviation of each vertex position 176 | :param model_name: name of the model, used to load and save the model 177 | """ 178 | super().__init__(model_name) 179 | self.audio_encoder = AudioEncoder(audio_dim) 180 | self.expression_encoder = ExpressionEncoder(expression_dim, n_vertices, mean, stddev) 181 | self.fusion_model = FusionMlp(classes, heads, expression_dim, audio_dim) 182 | 183 | def fuse(self, expression_code: th.Tensor, audio_code: th.Tensor): 184 | """ 185 | :param expression_code: B x T x expression_dim Tensor containing the expression encodings 186 | :param audio_code: B x T x audio_dim Tensor containing the audio encodings 187 | :return: logprobs: B x T x heads x classes Tensor containing logprobs for each categorical head 188 | """ 189 | logprobs = self.fusion_model(expression_code, audio_code)["logprobs"] 190 | return {"logprobs": logprobs} 191 | 192 | def encode(self, geom: th.Tensor, audio: th.Tensor): 193 | """ 194 | :param geom: B x T x n_vertices x 3 Tensor containing face geometries (expressions) 195 | :param audio: B x T x 1 x 16000 Tensor containing one second of 16kHz audio centered around each frame 1,...,T 196 | :return: expression_code: B x T x expression_dim Tensor containing expression encoding 197 | audio_code: B x T x audio_dim Tensor containing audio encoding 198 | """ 199 | expression_code = self.expression_encoder(geom)["code"] 200 | audio_code = self.audio_encoder(audio)["code"] 201 | return {"expression_code": expression_code, "audio_code": audio_code} 202 | 203 | def forward(self, geom: th.Tensor, audio: th.Tensor): 204 | """ 205 | :param geom: B x T x n_vertices x 3 Tensor containing face geometries (expressions) 206 | :param audio: B x T x 1 x 16000 Tensor containing one second of 16kHz audio centered around each frame 1,...,T 207 | :return: logprobs: B x T x heads x classes Tensor containing logprobs for each categorical head 208 | expression_code: B x T x expression_dim Tensor containing expression encoding 209 | audio_code: B x T x audio_dim Tensor containing audio encoding 210 | """ 211 | codes = self.encode(geom, audio) 212 | logprobs = self.fuse(codes["expression_code"], codes["audio_code"])["logprobs"] 213 | 214 | return {"logprobs": logprobs, "expression_code": codes["expression_code"], "audio_code": codes["audio_code"]} 215 | -------------------------------------------------------------------------------- /models/context_model.py: -------------------------------------------------------------------------------- 1 | """ 2 | Copyright (c) Facebook, Inc. and its affiliates. 3 | All rights reserved. 4 | This source code is licensed under the license found in the 5 | LICENSE file in the root directory of this source tree. 6 | """ 7 | 8 | import torch as th 9 | import torch.nn.functional as F 10 | 11 | from utils.helpers import Net 12 | 13 | class MaskedContextConvolution(th.nn.Module): 14 | def __init__(self, ch_in: int, ch_out: int, heads: int, audio_dim: int, kernel_size: int = 1, dilation: int = 1): 15 | """ 16 | :param ch_in: number of input channels to the layer 17 | :param ch_out: number of output channels to the layer 18 | :param heads: number of heads 19 | :param audio_dim: size of the latent audio embedding 20 | :param kernel_size: kernel size of the convolution 21 | :param dilation: dilation used in the convolution 22 | """ 23 | super().__init__() 24 | self.ch_in = ch_in 25 | self.ch_out = ch_out 26 | self.heads = heads 27 | self.audio_dim = audio_dim 28 | self.kernel_size = kernel_size 29 | self.dilation = dilation 30 | 31 | self.unmasked_linear = th.nn.Conv1d(audio_dim, ch_out * heads, kernel_size=1) 32 | 33 | self.masked_linear = th.nn.Conv1d(ch_in * heads, ch_out * heads, kernel_size=1) 34 | mask = th.ones(ch_out * heads, ch_in * heads, 1) 35 | for i in range(heads): 36 | mask[ch_out * i:ch_out * (i+1), ch_in * i:, :] = 0 37 | self.register_buffer("mask", mask) 38 | 39 | if kernel_size > 0: 40 | self.historic = th.nn.Conv1d(ch_in * heads, ch_out * heads, kernel_size=kernel_size, dilation=dilation) 41 | 42 | self.reset() 43 | 44 | def receptive_field(self): 45 | """ 46 | :return: receptive field of the layer 47 | """ 48 | if self.kernel_size == 0: 49 | return 1 50 | else: 51 | return self.dilation * (self.kernel_size - 1) + 2 52 | 53 | def reset(self): 54 | """ 55 | reset buffer before animating a new sequence 56 | """ 57 | self.buffer = th.zeros(1, self.heads * self.ch_out, 0) 58 | self.historic_t = -1 59 | 60 | def forward_inference(self, t: int, h: int, context: th.Tensor, audio: th.Tensor = None): 61 | """ 62 | :param t: current time step 63 | :param h: current head 64 | :param context: B x T x heads x ch_in Tensor 65 | :param audio: B x T x audio_dim Tensor 66 | :return: B x T x heads x ch_out Tensor 67 | """ 68 | B, T = context.shape[0], context.shape[1] 69 | context = context.view(B, T, -1).permute(0, 2, 1).contiguous() 70 | 71 | if self.historic_t < t: 72 | self.buffer = self.buffer.to(context.device) 73 | self.buffer = th.cat([self.buffer, th.zeros(1, self.buffer.shape[1], 1, device=self.buffer.device)], dim=-1) 74 | 75 | # next head from previous head predictions 76 | y_masked = self.masked_linear.bias[h*self.ch_out:(h+1)*self.ch_out].view(1, -1, 1).clone() 77 | if h > 0: 78 | y_masked += F.conv1d(context[:, :h*self.ch_in, -1:], 79 | self.masked_linear.weight[h*self.ch_out:(h+1)*self.ch_out, :h*self.ch_in, :]) 80 | self.buffer[:, h*self.ch_out:(h+1)*self.ch_out, -1:] += y_masked 81 | 82 | # next head from audio 83 | if audio is not None: 84 | audio = audio[:, -1:, :] 85 | audio = audio.permute(0, 2, 1).contiguous() 86 | y_audio = F.conv1d(audio[:, :, -1:], 87 | self.unmasked_linear.weight[h*self.ch_out:(h+1)*self.ch_out, :, :], 88 | bias=self.unmasked_linear.bias[h*self.ch_out:(h+1)*self.ch_out]) 89 | self.buffer[:, h*self.ch_out:(h+1)*self.ch_out, -1:] += y_audio 90 | 91 | # historic time steps 92 | if self.kernel_size > 0 and self.historic_t < t: 93 | h = context[:, :, -self.receptive_field():-1] 94 | if h.shape[-1] < self.receptive_field() - 1: 95 | h = F.pad(h, pad=[self.receptive_field() - 1 - h.shape[-1], 0]) 96 | h = self.historic(h) 97 | self.buffer[:, :, -1:] += h 98 | 99 | self.historic_t = t 100 | 101 | return self.buffer.permute(0, 2, 1).contiguous().view(1, -1, self.heads, self.ch_out) 102 | 103 | 104 | def forward(self, context: th.Tensor, audio: th.Tensor = None): 105 | """ 106 | :param context: B x T x heads x ch_in Tensor 107 | :param audio: B x T x audio_dim Tensor 108 | :return: B x T x heads x ch_out Tensor 109 | """ 110 | B, T = context.shape[0], context.shape[1] 111 | context = context.view(B, T, -1).permute(0, 2, 1).contiguous() 112 | 113 | # current context time step: masked along head axis 114 | y = F.conv1d(context, self.masked_linear.weight * self.mask, bias=self.masked_linear.bias) 115 | 116 | # current audio time step: no masking 117 | if audio is not None: 118 | audio = audio.permute(0, 2, 1).contiguous() 119 | audio = self.unmasked_linear(audio) 120 | y = y + audio 121 | 122 | # historic time steps 123 | if self.kernel_size > 0: 124 | h = F.pad(context[:, :, :-1], [self.dilation * (self.kernel_size - 1) + 1, 0]) 125 | y = y + self.historic(h) 126 | 127 | y = y.permute(0, 2, 1).contiguous().view(B, T, self.heads, self.ch_out) 128 | 129 | return y 130 | 131 | 132 | class ContextModel(Net): 133 | def __init__(self, classes: int = 128, heads: int = 64, audio_dim: int = 128, model_name: str = "context_model"): 134 | """ 135 | :param classes: number of classes for the categorical latent embedding 136 | :param heads: number of heads for the categorical latent embedding 137 | :param audio_dim: size of the latent audio embedding 138 | :param model_name: name of the model, used to load and save the model 139 | """ 140 | super().__init__(model_name) 141 | self.classes = classes 142 | self.heads = heads 143 | self.audio_dim = audio_dim 144 | 145 | hidden = 64 146 | self.embedding = MaskedContextConvolution(ch_in=classes, ch_out=hidden, heads=heads, audio_dim=audio_dim, kernel_size=0) 147 | self.context_layers = th.nn.ModuleList([ 148 | MaskedContextConvolution(ch_in=hidden, ch_out=hidden, heads=heads, audio_dim=audio_dim, kernel_size=2, dilation=1), 149 | MaskedContextConvolution(ch_in=hidden, ch_out=hidden, heads=heads, audio_dim=audio_dim, kernel_size=2, dilation=2), 150 | MaskedContextConvolution(ch_in=hidden, ch_out=hidden, heads=heads, audio_dim=audio_dim, kernel_size=2, dilation=4), 151 | MaskedContextConvolution(ch_in=hidden, ch_out=hidden, heads=heads, audio_dim=audio_dim, kernel_size=2, dilation=8), 152 | ]) 153 | self.logits = MaskedContextConvolution(ch_in=hidden, ch_out=classes, heads=heads, audio_dim=audio_dim, kernel_size=0) 154 | 155 | def receptive_field(self): 156 | """ 157 | :return: receptive field of the model 158 | """ 159 | receptive_field = 1 160 | for layer in self.context_layers: 161 | receptive_field += layer.receptive_field() - 1 162 | return receptive_field 163 | 164 | def _reset(self): 165 | """ 166 | reset buffers in each layer before animating a new sequence 167 | """ 168 | self.embedding.reset() 169 | for layer in self.context_layers: 170 | layer.reset() 171 | self.logits.reset() 172 | 173 | def _forward_inference(self, t: int, h: int, context: th.Tensor, audio: th.Tensor): 174 | """ 175 | :param t: current time step 176 | :param h: current head 177 | :param context: B x T x heads x classes Tensor 178 | :param audio: B x T x audio_dim Tensor 179 | :return: logprobs: B x T x heads x classes Tensor containing log probabilities for each class 180 | probs: B x T x heads x classes Tensor containing probabilities for each class 181 | labels: B x T x heads LongTensor containing discretized class labels 182 | """ 183 | x = self.embedding.forward_inference(t, h, context) 184 | 185 | for layer in self.context_layers: 186 | x = layer.forward_inference(t, h, x, audio) 187 | x = F.leaky_relu(x, 0.2) 188 | 189 | logits = self.logits.forward_inference(t, h, x) 190 | logprobs = F.log_softmax(logits, dim=-1) 191 | probs = F.softmax(logprobs, dim=-1) 192 | labels = th.argmax(logprobs, dim=-1) 193 | 194 | return {"logprobs": logprobs, "probs": probs, "labels": labels} 195 | 196 | 197 | def sample(self, audio_code: th.Tensor, argmax: bool = False): 198 | """ 199 | :param audio_code: B x T x audio_dim Tensor containing the encoded audio for the sequence 200 | :param argmax: if False, sample from Gumbel softmax; if True use classes with highest probabilities 201 | :return: B x T x heads x classes Tensor containing one-hot representation of latent code 202 | """ 203 | assert audio_code.shape[0] == 1 204 | T = audio_code.shape[1] 205 | one_hot = th.zeros(1, T, self.heads, self.classes, device=audio_code.device) 206 | self._reset() 207 | for t in range(T): 208 | start, end = max(0, t - self.receptive_field()), t + 1 209 | context = one_hot[:, start:end, :, :] 210 | audio = audio_code[:, start:end, :] 211 | for h in range(self.heads): 212 | # select input for next logprobs 213 | logprobs = self._forward_inference(t, h, context, audio)["logprobs"][:, -1, h, :] 214 | # discretize 215 | if not argmax: 216 | g = -th.log(-th.log(th.clamp(th.rand(logprobs.shape, device=logprobs.device), min=1e-10, max=1))) 217 | logprobs = logprobs + g 218 | label_idx = th.argmax(logprobs, dim=-1).squeeze().item() 219 | one_hot[:, t, h, label_idx] = 1 220 | return {"one_hot": one_hot} 221 | 222 | def forward(self, expression_one_hot: th.Tensor, audio_code: th.Tensor): 223 | """ 224 | :param expression_one_hot: B x T x heads x classes Tensor containing one hot representation of previous labels 225 | audio_code: B x T x audio_dim Tensor containing the audio embedding 226 | :return: logprobs: B x T x heads x C Tensor containing log probabilities for each class 227 | probs: B x T x heads x C Tensor containing probabilities for each class 228 | labels: B x T x heads LongTensor containing label indices 229 | """ 230 | 231 | x = self.embedding(expression_one_hot) 232 | 233 | for layer in self.context_layers: 234 | x = layer(x, audio_code) 235 | x = F.leaky_relu(x, 0.2) 236 | 237 | logits = self.logits(x) 238 | logprobs = F.log_softmax(logits, dim=-1) 239 | probs = F.softmax(logprobs, dim=-1) 240 | labels = th.argmax(logprobs, dim=-1) 241 | 242 | return {"logprobs": logprobs, "probs": probs, "labels": labels} 243 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Attribution-NonCommercial 4.0 International 2 | 3 | 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For 396 | the avoidance of doubt, this paragraph does not form part of the 397 | public licenses. 398 | 399 | Creative Commons may be contacted at creativecommons.org. -------------------------------------------------------------------------------- /assets/eye_keypoints.txt: -------------------------------------------------------------------------------- 1 | 0 2 | 0 3 | 0 4 | 0 5 | 0 6 | 0 7 | 0 8 | 0 9 | 0 10 | 0 11 | 0 12 | 0 13 | 0 14 | 0 15 | 0 16 | 1 17 | 0 18 | 0 19 | 0 20 | 0 21 | 0 22 | 0 23 | 0 24 | 0 25 | 0 26 | 0 27 | 0 28 | 0 29 | 0 30 | 0 31 | 0 32 | 0 33 | 0 34 | 0 35 | 0 36 | 0 37 | 0 38 | 0 39 | 0 40 | 0 41 | 0 42 | 0 43 | 0 44 | 0 45 | 0 46 | 0 47 | 0 48 | 0 49 | 0 50 | 0 51 | 0 52 | 0 53 | 0 54 | 0 55 | 0 56 | 0 57 | 0 58 | 0 59 | 0 60 | 0 61 | 0 62 | 0 63 | 0 64 | 0 65 | 0 66 | 0 67 | 0 68 | 0 69 | 0 70 | 0 71 | 0 72 | 0 73 | 0 74 | 0 75 | 0 76 | 0 77 | 0 78 | 0 79 | 0 80 | 0 81 | 0 82 | 0 83 | 1 84 | 0 85 | 0 86 | 0 87 | 0 88 | 0 89 | 0 90 | 0 91 | 0 92 | 0 93 | 0 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