├── survey.pdf ├── LICENSE └── README.md /survey.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/cwchenwang/awesome-3d-diffusion/HEAD/survey.pdf -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2023 Chen Wang 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Awesome 3D Diffusion 2 | This repo collects papers that use diffusion models for 3D generation. 3 | 4 | 🔥🔥🔥 Check out our collection of papers on 4D generation: https://github.com/cwchenwang/awesome-4d-generation 5 | 6 | 🔥🔥🔥 Please take a look at our survey on diffusion models for 3D Generation, which gives a summary of the papers of this list: https://github.com/cwchenwang/awesome-3d-diffusion/blob/main/survey.pdf 7 | 8 | If you consider our paper or list useful, please cite our paper: 9 | ``` 10 | @article{wang2024diffusion, 11 | title={Diffusion Models for 3D Generation: A Survey}, 12 | author={Wang, Chen and Peng, Hao-Yang and Liu, Ying-Tian and Gu, Jiatao and Hu, Shi-Min}, 13 | howpublished = {\url{https://github.com/cwchenwang/awesome-3d-diffusion}}, 14 | year={2024} 15 | } 16 | ``` 17 | 18 | **Note**: This list is far from complete, please directly open a pull request if you want to add a paper or modify the information. You don't need to open an issue. 19 | 20 | ## Table of Contents 21 | - [2D Diffusion with Pretraining](#2d-diffusion-with-pretraining) 22 | * [Text-to-3D Object Generation](#text-to-3d-object-generation) 23 | * [Compositional or Scene Generation](#text-to-3d-scene-generation) 24 | * [Image-to-3D](#image-to-3d) 25 | * [Human and Animal](#human-and-animal) 26 | * [3D Editing](#3d-editing) 27 | * [Texturing](#texturing) 28 | * [Multi-view Diffusion](#multi-view-diffusion) 29 | - [2D Diffusion without Pretraining](#2d-diffusion-without-pretraining) 30 | * [3D Objects](#3d-objects) 31 | * [3D Scenes](#3d-scenes) 32 | - [Diffusion in 3D Space](#diffusion-in-3d-space) 33 | * [Explicit Representation](#explicit-representation) 34 | * [Implicit Representation](#implicit-representation) 35 | * [Triplane](#triplane) 36 | * [Latent Representation](#latent-representation) 37 | * [Novel Representations](#novel-representations) 38 | - [Diffusion for Motion](#diffusion-for-motion) 39 | * [Human Motion](#human-motion) 40 | 41 | 42 | 43 | ## 2D Diffusion with Pretraining 44 | ### Text-to-3D Object Generation 45 | - [DreamFusion: Text-to-3D using 2D Diffusion](https://arxiv.org/abs/2209.14988), Poole et al., Arxiv 2022 46 | - [Magic3D: High-Resolution Text-to-3D Content Creation](https://arxiv.org/abs/2211.10440), Lin et al., Arxiv 2022 47 | - [Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation](https://arxiv.org/abs/2212.00774), Wang et al., Arxiv 2022 48 | - [Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creation](https://arxiv.org/abs/2303.13873), Chen et al., Arxiv 2023 49 | - [Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generation](https://arxiv.org/abs/2303.07937), Seo et al., Arxiv 2023 50 | - [DITTO-NeRF: Diffusion-based Iterative Text To Omni-directional 3D Model](https://arxiv.org/abs/2304.02827), Seo et al., Arxiv 2023 51 | - [TextMesh: Generation of Realistic 3D Meshes From Text Prompts](https://arxiv.org/abs/2304.12439), Tsalicoglou et al., Arxiv 2023 52 | - [Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generation](https://arxiv.org/abs/2303.07937), Seo et al., Arxiv 2023 53 | - [Text-driven Visual Synthesis with Latent Diffusion Prior](https://arxiv.org/abs/2302.08510), Liao et al., Arxiv 2023 54 | - [Re-imagine the Negative Prompt Algorithm: Transform 2D Diffusion into 3D, alleviate Janus problem and Beyond](https://arxiv.org/abs/2304.04968), Armandpour et al., Arxiv 2023 55 | - [HiFA: High-fidelity Text-to-3D with Advanced Diffusion Guidance](https://arxiv.org/abs/2305.18766), Zhu and Zhuang, Arxiv 2023 56 | - [ATT3D: Amortized Text-to-3D Object Synthesis](https://arxiv.org/abs/2306.07349), Lorraine et al., Arxiv 2023 57 | - [PanoGen: Text-Conditioned Panoramic Environment Generation for Vision-and-Language Navigation](https://arxiv.org/abs/2305.19195), Li and Bansal, Arxiv 2023 58 | - [ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation](https://arxiv.org/abs/2305.16213), Wang et al., NeurIPS 2023. 59 | - [DreamTime: An Improved Optimization Strategy for Text-to-3D Content Creation](http://arxiv.org/abs/2306.12422), Huang et al., Arxiv 2023 60 | - [EfficientDreamer: High-Fidelity and Robust 3D Creation via Orthogonal-view Diffusion Prior](https://arxiv.org/abs/2308.13223), Zhao et al., Arxiv 2023 61 | - [SweetDreamer: Aligning Geometric Priors in 2D Diffusion for Consistent Text-to-3D](https://arxiv.org/abs/2309.03453), Li et al., Arxiv 2023 62 | - [DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior](http://arxiv.org/abs/2310.16818), Sun et al., Arxiv 2023 63 | - [Text-to-3D with Classifier Score Distillation](http://arxiv.org/abs/2310.19415), Yu et al., Arxiv 2023 64 | - [Instant3D: Instant Text-to-3D Generation](http://arxiv.org/abs/2311.08403), Li et al., Arxiv 2023 65 | - [HyperFields: Towards Zero-Shot Generation of NeRFs from Text](http://arxiv.org/abs/2310.17075), Babu et al., ICML 2024 66 | - [DreamSpace: Dreaming Your Room Space with Text-Driven Panoramic Texture Propagation](http://arxiv.org/abs/2310.13119), Yang et al., Arxiv 2023 67 | - [Enhancing High-Resolution 3D Generation through Pixel-wise Gradient Clipping](http://arxiv.org/abs/2310.12474), Pan et al., Arxiv 2023 68 | - [GaussianDreamer: Fast Generation from Text to 3D Gaussian Splatting with Point Cloud Priors](http://arxiv.org/abs/2310.08529), Yi et al., Arxiv 2023 69 | - [LucidDreamer: Domain-free Generation of 3D Gaussian Splatting Scenes](https://arxiv.org/abs/2311.13384), Chung et al., Arxiv 2023 70 | - [CG3D: Compositional Generation for Text-to-3D via Gaussian Splatting](http://arxiv.org/abs/2311.17907), Vilesov et al., Arxiv 2023 71 | - [LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score Matching](http://arxiv.org/abs/2311.11284), Liang et al., Arxiv 2023 72 | - [StableDreamer: Taming Noisy Score Distillation Sampling for Text-to-3D](http://arxiv.org/abs/2312.02189), Guo et al., Arxiv 2023 73 | - [DreamComposer: Controllable 3D Object Generation via Multi-View Conditions](http://arxiv.org/abs/2312.03611), Yang et al., Arxiv 2023 74 | - [GraphDreamer: Compositional 3D Scene Synthesis from Scene Graphs](http://arxiv.org/abs/2312.00093), Gao et al., Arxiv 2023 75 | - [X-Dreamer: Creating High-quality 3D Content by Bridging the Domain Gap Between Text-to-2D and Text-to-3D Generation](http://arxiv.org/abs/2312.00085), Ma et al., Arxiv 2023 76 | - [HyperDreamer: Hyper-Realistic 3D Content Generation and Editing from a Single Image](https://arxiv.org/abs/2312.04543), Wu et al., SIGGRAPH ASIA 2023 77 | - [RichDreamer: A Generalizable Normal-Depth Diffusion Model for Detail Richness in Text-to-3D](https://arxiv.org/abs/2311.16918), Qiu et al., Arxiv 2023 78 | - [Text-to-3D Generation with Bidirectional Diffusion using both 2D and 3D priors](https://arxiv.org/abs/2312.04963), Ding et al., Arxiv 2023 79 | - [Sherpa3D: Boosting High-Fidelity Text-to-3D Generation via Coarse 3D Prior](https://arxiv.org/abs/2312.06655), Liu et al., Arxiv 2023 80 | - [DreamPropeller: Supercharge Text-to-3D Generation with Parallel Sampling](https://arxiv.org/abs/2311.17082), Zhou et al., Arxiv 2023 81 | - [UniDream: Unifying Diffusion Priors for Relightable Text-to-3D Generation](https://arxiv.org/abs/2312.08754), Liu et al., Arxiv 2023 82 | - [Stable Score Distillation for High-Quality 3D Generation](https://arxiv.org/abs/2312.09305), Tang et al., Arxiv 2023 83 | - [DreamControl: Control-Based Text-to-3D Generation with 3D Self-Prior](https://arxiv.org/abs/2312.06439), Huang et al., CVPR 2024 84 | - [Text-Image Conditioned Diffusion for Consistent Text-to-3D Generation](https://arxiv.org/abs/2312.11774), He et al., Arxiv 2023 85 | - [HexaGen3D: StableDiffusion is just one step away from Fast and Diverse Text-to-3D Generation](https://arxiv.org/abs/2401.07727), Mercier et al., Arxiv 2024 86 | - [Repaint123: Fast and High-quality One Image to 3D Generation with Progressive Controllable 2D Repainting](https://arxiv.org/abs/2312.13271), Zhang et al., Arxiv 2023 87 | - [Retrieval-Augmented Score Distillation for Text-to-3D Generation](https://arxiv.org/abs/2402.02972), Seo et al., Arxiv 2024 88 | - [BrightDreamer: Generic 3D Gaussian Generative Framework for Fast Text-to-3D Synthesis](https://arxiv.org/abs/2403.11273), Jiang and Wang, Arxiv 2024 89 | - [DreamReward: Text-to-3D Generation with Human Preference](https://arxiv.org/abs/2403.14613), Ye et al., Arxiv 2024 90 | - [DreamFlow: High-Quality Text-to-3D Generation by Approximating Probability Flow](https://arxiv.org/abs/2403.14966), Lee et al., ICLR 2024 91 | - [LATTE3D: Large-scale Amortized Text-To-Enhanced3D Synthesis](http://arxiv.org/abs/2403.15385), Xie et al., Arix 2024 92 | - [DreamPolisher: Towards High-Quality Text-to-3D Generation via Geometric Diffusion](https://arxiv.org/abs/2403.17237), Lin et al., Arxiv 2024 93 | - [VP3D: Unleashing 2D Visual Prompt for Text-to-3D Generation](https://arxiv.org/abs/2403.17001), Chen et al., CVPR 2024 94 | - [Hash3D: Training-free Acceleration for 3D Generation](https://arxiv.org/abs/2404.06091), Yang and Wang, Arxiv 2024 95 | - [MicroDreamer: Zero-shot 3D Generation in ∼20 Seconds by Score-based Iterative Reconstruction](https://arxiv.org/abs/2404.19525), Chen et al., Arxiv 2024 96 | - [SketchDream: Sketch-based Text-to-3D Generation and Editing](https://arxiv.org/pdf/2405.06461), Liu et al., Arxiv 2024 97 | - [Flow Score Distillation for Diverse Text-to-3D](https://arxiv.org/pdf/2405.10988), Yan et al., Arxiv 2024 98 | - [Dreamer XL: Towards High-Resolution Text-to-3D Generation via Trajectory Score Matching](https://arxiv.org/abs/2405.11252), Miao et al., Arxiv 2024 99 | - [Atlas3D: Physically Constrained Self-Supporting Text-to-3D for Simulation and Fabrication](https://arxiv.org/abs/2405.18515), Chen et al., Arxiv 2024 100 | - [DIRECT-3D: Learning Direct Text-to-3D Generation on Massive Noisy 3D Data](https://arxiv.org/pdf/2406.04322), Liu et al., Arxiv 2024 101 | - [Text-guided Controllable Mesh Refinement for Interactive 3D Modeling](https://arxiv.org/pdf/2406.01592), Chen et al., Arxiv 2024 102 | - [PlacidDreamer: Advancing Harmony in Text-to-3D Generation](https://arxiv.org/pdf/2407.13976), Huang et al., ACM MM 2024 103 | - [JointDreamer: Ensuring Geometry Consistency and Text Congruence in Text-to-3D Generation via Joint Score Distillation](https://arxiv.org/pdf/2407.12291), Jiang et al., ECCV 2024 104 | - [DreamDissector: Learning Disentangled Text-to-3D Generation from 2D Diffusion Priors](https://arxiv.org/pdf/2407.16260), Yan et al., ECCV 2024 105 | - [Connecting Consistency Distillation to Score Distillation for Text-to-3D Generation](https://arxiv.org/pdf/2407.13584), Li et al., ECCV 2024 106 | - [PlacidDreamer: Advancing Harmony in Text-to-3D Generation](https://arxiv.org/abs/2407.13976), Huang et al., Arxiv 2024 107 | - [DreamCouple: Exploring High Quality Text-to-3D Generation Via Rectified Flow](https://arxiv.org/abs/2408.05008), Li et al., Arxiv 2024 108 | - [ScaleDreamer: Scalable Text-to-3D Synthesis with Asynchronous Score Distillation](https://arxiv.org/pdf/2407.02040), Ma et al., ECCV 2024 109 | - [Vista3D: Unravel the 3D Darkside of a Single Image](https://arxiv.org/pdf/2409.12193), Shen et al., ECCV 2024 110 | - [SceneDreamer360: Text-Driven 3D-Consistent Scene Generation with Panoramic Gaussian Splatting](https://arxiv.org/abs/2408.13711), Li et al., Arxiv 2024 111 | 112 | ### Compositional or Scene Generation 113 | - [Text2Room: Extracting Textured 3D Meshes from 2D Text-to-Image Models](https://arxiv.org/abs/2303.11989), Höllein et al., Arxiv 2023 114 | - [SceneScape: Text-Driven Consistent Scene Generation](https://arxiv.org/abs/2302.01133), Fridman et al., Arxiv 2023 115 | - [Compositional 3D Scene Generation using Locally Conditioned Diffusion](https://arxiv.org/abs/2303.12218), Po and Wetzstein, Arxiv 2023 116 | - [Set-the-Scene: Global-Local Training for Generating Controllable NeRF Scenes](https://arxiv.org/abs/2303.13450), Cohen-Bar et al., Arxiv 2023 117 | - [CompoNeRF: Text-guided Multi-object Compositional NeRF with Editable 3D Scene Layout](https://arxiv.org/abs/2303.13843), Lin et al., Arxiv 2023 118 | - [Text2NeRF: Text-Driven 3D Scene Generation with Neural Radiance Fields](https://arxiv.org/abs/2305.11588), Zhang et al., Arxiv 2023 119 | - [Ctrl-Room: Controllable Text-to-3D Room Meshes Generation with Layout Constraints](https://arxiv.org/abs/2310.03602), Fang et al., Arxiv 2023 120 | - [SceneWiz3D: Towards Text-guided 3D Scene Composition](https://arxiv.org/abs/2312.08885), Zhang et al., Arxiv 2023 121 | - [ShowRoom3D: Text to High-Quality 3D Room Generation Using 3D Priors](https://arxiv.org/abs/2312.13324), Mao et al., Arxiv 2023 122 | - [Text2Street: Controllable Text-to-image Generation for Street Views](https://arxiv.org/abs/2402.04504), Su et al., Arxiv 2024 123 | - [GALA3D: Towards Text-to-3D Complex Scene Generation via Layout-guided Generative Gaussian Splatting](http://arxiv.org/abs/2402.07207), Zhou et al., Arxiv 2023 124 | - [Sketch2NeRF: Multi-view Sketch-guided Text-to-3D Generation](https://arxiv.org/abs/2401.14257), Chen et al., Arxiv 2024 125 | - [A Quantitative Evaluation of Score Distillation Sampling Based Text-to-3D](https://arxiv.org/abs/2402.18780), Fei et al., Arxiv 2024 126 | - [DreamScene: 3D Gaussian-based Text-to-3D Scene Generation via Formation Pattern Sampling](https://arxiv.org/abs/2404.03575), Li et al., Arxiv 2024 127 | - [DreamScene360: Unconstrained Text-to-3D Scene Generation with Panoramic Gaussian Splatting](https://arxiv.org/abs/2404.06903), Zhou et al., Arxiv 2024 128 | - [RealmDreamer: Text-Driven 3D Scene Generation with Inpainting and Depth Diffusion](https://arxiv.org/abs/2404.07199), Shriram et al., Arxiv 2024 129 | - [Urban Architect: Steerable 3D Urban Scene Generation with Layout Prior](https://arxiv.org/abs/2404.06780), Lu et al., Arxiv 2024 130 | - [DreamScape: 3D Scene Creation via Gaussian Splatting joint Correlation Modeling](https://arxiv.org/abs/2404.09227), Yang et al., Arxiv 2024 131 | - [Invisible Stitch: Generating Smooth 3D Scenes with Depth Inpainting](https://arxiv.org/abs/2404.19758), Engstler et al., Arxiv 2024 132 | - [REPARO: Compositional 3D Assets Generation with Differentiable 3D Layout Alignment](https://arxiv.org/pdf/2405.18525), Han et al., Arxiv 2024 133 | - [VividDream: Generating 3D Scene with Ambient Dynamics](https://arxiv.org/pdf/2405.20334), Lee et al., Arxiv 2024 134 | - [VividDreamer: Towards High-Fidelity and Efficient Text-to-3D Generation](https://arxiv.org/abs/2406.14964), Chen et al., Arxiv 2024 135 | - [Director3D: Real-world Camera Trajectory and 3D Scene Generation from Text](https://arxiv.org/pdf/2406.17601), Li et al., Arxiv 2024 136 | - [Sketch2Scene: Automatic Generation of Interactive 3D Game Scenes from User's Casual Sketches](https://arxiv.org/abs/2408.04567), Xu et al., Arxiv 2024 137 | - [Scene123: One Prompt to 3D Scene Generation via Video-Assisted and Consistency-Enhanced MAE](https://arxiv.org/pdf/2408.05477), Yang et al., Arxiv 2024 138 | - [HoloDreamer: Holistic 3D Panoramic World Generation from Text Descriptions](https://arxiv.org/pdf/2407.15187), Zhou et al., Arxiv 2024 139 | - [COMOGen: A Controllable Text-to-3D Multi-object Generation Framework](https://arxiv.org/abs/2409.00590), Sun et al., Arxiv 2024 140 | - [DynamicCity: Large-Scale 4D Occupancy Generation from Dynamic Scenes](https://arxiv.org/abs/2410.18084), Bian et al., ICLR 2025 Spotlight 141 | 142 | ### Image-to-3D 143 | - [NeuralLift-360: Lifting An In-the-wild 2D Photo to A 3D Object with $360^{\deg}$ Views](https://arxiv.org/abs/2211.16431), Xu et al., CVPR 2023 144 | - [NeRDi: Single-View NeRF Synthesis with Language-Guided Diffusion as General Image Priors](https://arxiv.org/abs/2212.03267), Deng et al., CVPR 2023 145 | - [Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures](https://arxiv.org/abs/2211.07600), Metzer et al., CVPR 2023 146 | - [RealFusion: 360{\deg} Reconstruction of Any Object from a Single Image](https://arxiv.org/abs/2302.10663), Melas-Kyriazi et al., Arxiv 2023 147 | - [Make-It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion Prior](https://arxiv.org/abs/2303.14184), Tang et al., Arxiv 2023 148 | - [Zero-1-to-3: Zero-shot One Image to 3D Object](https://arxiv.org/abs/2303.11328), Liu et al., Arxiv 2023 149 | - [DreamBooth3D: Subject-Driven Text-to-3D Generation](https://arxiv.org/abs/2303.13508), Raj et al., Arxiv 2023 150 | - [DreamSparse: Escaping from Plato's Cave with 2D Frozen Diffusion Model Given Sparse Views](https://arxiv.org/abs/2306.03414), Yoo et al., Arxiv 2023 151 | - [One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization](https://arxiv.org/abs/2306.16928), Liu et al., Arxiv 2023 152 | - [Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors](https://arxiv.org/abs/2306.17843), Qian et al., Arxiv 2023 153 | - [360◦ Reconstruction From a Single Image Using Space Carved Outpainting](https://arxiv.org/abs/2309.10279), Ryu et al., SIGGRAPH ASIA 2023 154 | - [Viewpoint Textual Inversion: Unleashing Novel View Synthesis with Pretrained 2D Diffusion Models](http://arxiv.org/abs/2309.07986), Burgess et al., Arxiv 2023 155 | - [IPDreamer: Appearance-Controllable 3D Object Generation with Image Prompts](https://arxiv.org/abs/2310.05375), Zeng et al., 156 | - [Customize-It-3D: High-Quality 3D Creation from A Single Image Using Subject-Specific Knowledge Prior](https://arxiv.org/abs/2312.11535), Huang et al., Arxiv 2023 157 | - [HarmonyView: Harmonizing Consistency and Diversity in One-Image-to-3D](http://arxiv.org/abs/2312.15980), Woo et al., Arxiv 2023 158 | - [Repaint123: Fast and High-quality One Image to 3D Generation with Progressive Controllable 2D Repainting](https://arxiv.org/abs/2312.13271), Zhang et al., Arxiv 2023 159 | - [AGG: Amortized Generative 3D Gaussians for Single Image to 3D](https://arxiv.org/abs/2401.04099), Xu et al., Arxiv 2024 160 | - [Part123: Part-aware 3D Reconstruction from a Single-view Image](https://arxiv.org/abs/2405.16888), Liu et al., SIGGRAPH Conference 2024 161 | - [GECO: Generation Image-to-3D within a Second](https://arxiv.org/abs/2405.20327), Wang et al., Arxiv 2024 162 | - [Fourier123: One Image to High-Quality 3D Object Generation with Hybrid Fourier Score Distillation](https://arxiv.org/pdf/2405.20669), Yang et al., Arxiv 2024 163 | - [Unique3D: High-Quality and Efficient 3D Mesh Generation from a Single Image](https://arxiv.org/pdf/2405.20343), Wu et al., Arxiv 2024 164 | 165 | ### Human and Animal 166 | - [ScoreHMR: Score-Guided Diffusion for 3D Human Recovery](https://github.com/statho/ScoreHMR), Stathopoulos et al., CVPR 2024 167 | - [Generative Proxemics: A Prior for 3D Social Interaction from Images](https://muelea.github.io/buddi/), Müller at al, CVPR 2024 168 | - [DreamFace: Progressive Generation of Animatable 3D Faces under Text Guidance](https://arxiv.org/abs/2304.03117), Zhang et al., Arxiv 2023 169 | - [AvatarCraft: Transforming Text into Neural Human Avatars with Parameterized Shape and Pose Control](https://arxiv.org/abs/2303.17606), Jiang et al., ICCV 2023 170 | - [DreamAvatar: Text-and-Shape Guided 3D Human Avatar Generation via Diffusion Models](https://arxiv.org/abs/2304.00916), Cao et al., Arxiv 2023 171 | - [DreamWaltz: Make a Scene with Complex 3D Animatable Avatars](https://arxiv.org/abs/2305.12529), Huang et al., Arxiv 2023 172 | - [ZeroAvatar: Zero-shot 3D Avatar Generation from a Single Image](https://arxiv.org/abs/2305.16411), Weng et al., Arxiv 2023 173 | - [AvatarBooth: High-Quality and Customizable 3D Human Avatar Generation](https://arxiv.org/abs/2306.09864), Zeng et al., Arxiv 2023 174 | - [Farm3D: Learning Articulated 3D Animals by Distilling 2D Diffusion](https://arxiv.org/pdf/2304.10535.pdf) Jakab et al., Arxiv 2023 175 | - [Anything 3D: Towards Single-view Anything Reconstruction in the Wild](https://arxiv.org/abs/2304.10261), Shen et al., Arxiv 2023 176 | - [ARTIC3D: Learning Robust Articulated 3D Shapes from Noisy Web Image Collections](https://arxiv.org/abs/2306.04619), Yao et al., Arxiv 2023 177 | - [TADA! Text to Animatable Digital Avatars](https://arxiv.org/abs/2308.10899), Liao et al., Arxiv 2023 178 | - [Diffusion-Guided Reconstruction of Everyday Hand-Object Interaction Clips](https://arxiv.org/abs/2309.05663), Ye et al., ICCV 2023 179 | - [Text-Guided Generation and Editing of Compositional 3D Avatars](http://arxiv.org/abs/2309.07125), Zhang et al., Arxiv 2023 180 | - [HumanGaussian: Text-Driven 3D Human Generation with Gaussian Splatting](https://arxiv.org/abs/2311.17061), Liu et al., Arxiv 2023 181 | - [AvatarStudio: High-fidelity and Animatable 3D Avatar Creation from Text](https://arxiv.org/abs/2311.17917), Zhang et al., Arxiv 2023 182 | - [Disentangled Clothed Avatar Generation from Text Descriptions](https://arxiv.org/abs/2312.05295), Wang et al., Arxiv 2023 183 | - [SEEAvatar: Photorealistic Text-to-3D Avatar Generation with Constrained Geometry and Appearance](https://arxiv.org/abs/2312.08889), Xu et al., Arxiv 2023 184 | - [GAvatar: Animatable 3D Gaussian Avatars with Implicit Mesh Learning](https://arxiv.org/abs/2312.11461), Yuan et al., Arxiv 2023 185 | - [Make-A-Character: High Quality Text-to-3D Character Generation within Minutes](http://arxiv.org/abs/2312.15430), Ren et al., Arxiv 2023 186 | - [Morphable Diffusion: 3D-Consistent Diffusion for Single-image Avatar Creation](https://arxiv.org/abs/2401.04728), Chen et al., Arxiv 2024 187 | 188 | ### 3D Editing 189 | - [SKED: Sketch-guided Text-based 3D Editing](https://arxiv.org/abs/2303.10735), Mikaeili et al., Arxiv 2023 190 | - [Instruct-NeRF2NeRF: Editing 3D Scenes with Instructions](https://arxiv.org/abs/2303.12789), Haque et al., ICCV 2023 191 | - [Instruct 3D-to-3D: Text Instruction Guided 3D-to-3D conversion](https://arxiv.org/abs/2303.15780), Kamata et al., Arxiv 2023 192 | - [Edit-DiffNeRF: Editing 3D Neural Radiance Fields using 2D Diffusion Model](https://arxiv.org/abs/2306.09551), Yu et al., Arxiv 2023 193 | - [Control4D: Dynamic Portrait Editing by Learning 4D GAN from 2D Diffusion-based Editor](https://arxiv.org/abs/2305.20082), Shao et al., Arxiv 2023 194 | - [RePaint-NeRF: NeRF Editting via Semantic Masks and Diffusion Models](https://arxiv.org/abs/2306.05668), Zhou et al., Arxiv 2023 195 | - [DreamEditor: Text-Driven 3D Scene Editing with Neural Fields](https://arxiv.org/abs/2306.13455), Zhuang et al., SIGRRAPH ASIA 2023 196 | - [Language-driven Object Fusion into Neural Radiance Fields with Pose-Conditioned Dataset Updates](https://arxiv.org/abs/2309.11281), Shum et al., Arxiv 2023 197 | - [ProteusNeRF: Fast Lightweight NeRF Editing using 3D-Aware Image Context](https://arxiv.org/abs/2310.09965), Wang et al., Arxiv 2023 198 | - [ED-NeRF: Efficient Text-Guided Editing of 3D Scene using Latent Space NeRF](https://arxiv.org/abs/2310.02712), Park et al., Arxiv 2023 199 | - [3D Paintbrush: Local Stylization of 3D Shapes with Cascaded Score Distillation](http://arxiv.org/abs/2311.09571), Decatur et al., Arxiv 2023 200 | - [GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splatting](https://arxiv.org/abs/2311.14521), Chen et al., Arxiv 2023 201 | - [Inpaint3D: 3D Scene Content Generation using 2D Inpainting Diffusion](http://arxiv.org/abs/2312.03869), Prabhu et al., Arxiv 2023 202 | - [NeRFiller: Completing Scenes via Generative 3D Inpainting](http://arxiv.org/abs/2312.04560), Weber et al., Arxiv 2023 203 | - [SHAP-EDITOR: Instruction-guided Latent 3D Editing in Seconds](https://arxiv.org/abs/2312.09246), Chen et al., Arxiv 2023 204 | - [LatentEditor: Text Driven Local Editing of 3D Scenes](https://arxiv.org/abs/2312.09313), Khalid et al., Arxiv 2023 205 | - [Free-Editor: Zero-shot Text-driven 3D Scene Editing](https://arxiv.org/abs/2312.13663), Karim et al., Arxiv 2023 206 | - [SIGNeRF: Scene Integrated Generation for Neural Radiance Fields](https://arxiv.org/abs/2401.01647), Dihlmann et al., Arxiv 2023 207 | - [Efficient-NeRF2NeRF: Streamlining Text-Driven 3D Editing with Multiview Correspondence-Enhanced Diffusion Models](https://arxiv.org/abs/2312.08563), Song et al., Arxiv 2024 208 | - [ReplaceAnything3D: Text-Guided 3D Scene Editing with Compositional Neural Radiance Fields](https://arxiv.org/abs/2401.17895), Bartrum et al., Arxiv 2024 209 | - [GaussCtrl: Multi-View Consistent Text-Driven 3D Gaussian Splatting Editing](http://arxiv.org/abs/2403.08733), Wu et al., Arxiv 2024 210 | - [View-Consistent 3D Editing with Gaussian Splatting](https://arxiv.org/abs/2403.11868), Wang et al., Arxiv 2024 211 | - [Interactive3D: Create What You Want by Interactive 3D Generation](https://arxiv.org/abs/2404.16510), Dong et al., Arxiv 2024 212 | - [DGE: Direct Gaussian 3D Editing by Consistent Multi-view Editing](https://arxiv.org/abs/2404.18929), Chen et al., Arxiv 2024 213 | - [DATENeRF: Depth-Aware Text-based Editing of NeRFs](https://arxiv.org/pdf/2404.04526), Rojas et al., Arxiv 2024 214 | 215 | ### Texturing 216 | - [TEXTure: Text-Guided Texturing of 3D Shapes](https://arxiv.org/abs/2302.01721), Richardson et al., Arxiv 2023 217 | - [Text2Tex: Text-driven Texture Synthesis via Diffusion Models](https://arxiv.org/abs/2303.11396), Chen et al., Arxiv 2023 218 | - [EucliDreamer: Fast and High-Quality Texturing for 3D Models with Stable Diffusion Depth](https://arxiv.org/abs/2311.15573), Le et al., Arxiv 2023 219 | - [Paint3D: Paint Anything 3D with Lighting-Less Texture Diffusion Models](http://arxiv.org/abs/2312.13913), Zeng et al., Arxiv 2023 220 | - [TextureDreamer: Image-guided Texture Synthesis through Geometry-aware Diffusion](https://arxiv.org/abs/2401.09416), Yeh et al., Arxiv 2024 221 | - [MaPa: Text-driven Photorealistic Material Painting for 3D Shapes](https://arxiv.org/abs/2404.17569), Zheng et al., Arxiv 2024 222 | - [DreamMat: High-quality PBR Material Generation with Geometry- and Light-aware Diffusion Models](https://arxiv.org/abs/2405.17176), Zhang et al., Arxiv 2024 223 | - [MatFuse: Controllable Material Generation with Diffusion Models](https://arxiv.org/abs/2308.11408) Vecchio et al., CVPR 2024 224 | - [StableMaterials: Enhancing Diversity in Material Generation via Semi-Supervised Learning](https://arxiv.org/abs/2406.09293) Vecchio et al., Arxiv 2024 225 | - [TexGen: Text-Guided 3D Texture Generation with Multi-view Sampling and Resampling](https://arxiv.org/abs/2408.01291), Huo et al., Arxiv 2024 226 | - [ControlMat: A Controlled Generative Approach to Material Capture](https://arxiv.org/abs/2309.01700) Vecchio et al., ACM ToG 2024 227 | 228 | ### Multi-view Diffusion 229 | - [MVDiffusion: Enabling Holistic Multi-view Image Generation with Correspondence-Aware Diffusion](https://arxiv.org/abs/2307.01097), Tang et al., Arxiv 2023 230 | - [MVDream: Multi-view Diffusion for 3D Generation](https://arxiv.org/abs/2308.16512), Shi et al., Arxiv 2023 231 | - [SyncDreamer: Generating Multiview-consistent Images from a Single-view Image](https://arxiv.org/abs/2309.03453), Liu et al., Arxiv 2023 232 | - [Consistent123: Improve Consistency for One Image to 3D Object Synthesis](http://arxiv.org/abs/2310.08092), Weng et al., Arxiv 2023 233 | - [Wonder3D: Single Image to 3D using Cross-Domain Diffusion](https://arxiv.org/abs/2310.15008), Long et al., Arxiv 2023 234 | - [Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model](http://arxiv.org/abs/2310.15110), Shi et al., Arxiv 2023 235 | - [TOSS:High-quality Text-guided Novel View Synthesis from a Single Image](http://arxiv.org/abs/2310.10644), Shi et al., Arxiv 2023 236 | - [Text-Guided Texturing by Synchronized Multi-View Diffusion](https://arxiv.org/abs/2311.12891), Liu et al., Arxiv 2023 237 | - [Direct2.5: Diverse Text-to-3D Generation via Multi-view 2.5D Diffusion](http://arxiv.org/abs/2311.15980), Lu et al., Arxiv 2023 238 | - [ViVid-1-to-3: Novel View Synthesis with Video Diffusion Models](http://arxiv.org/abs/2312.01305), Kwak et al., Arxiv 2023 239 | - [EpiDiff: Enhancing Multi-View Synthesis via Localized Epipolar-Constrained Diffusion](https://arxiv.org/abs/2312.06725), Huang et al., CVPR 2024 240 | - [BoostDream: Efficient Refining for High-Quality Text-to-3D Generation from Multi-View Diffusion](https://arxiv.org/abs/2401.16764), Yu et al., Arxiv 2024 241 | - [EscherNet: A Generative Model for Scalable View Synthesis](https://arxiv.org/abs/2402.03908), Kong et al., Arxiv 2024 242 | - [LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation](https://arxiv.org/abs/2402.05054), Tang et al., Arxiv 2024 243 | - [SPAD : Spatially Aware Multiview Diffusers](https://arxiv.org/abs/2402.05235), Kant et al., Arxiv 2024 244 | - [IM-3D: Iterative Multiview Diffusion and Reconstruction for High-Quality 3D Generation](https://arxiv.org/pdf/2402.08682), Melas-Kyriazi et al., Arxiv 2024 245 | - [MVDiffusion++: A Dense High-resolution Multi-view Diffusion Model for Single or Sparse-view 3D Object Reconstruction](https://arxiv.org/abs/2402.12712), Tang et al., Arxiv 2024 246 | - [CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model](http://arxiv.org/abs/2403.05034), Wang et al., Arxiv 2024 247 | - [V3D: Video Diffusion Models are Effective 3D Generators](https://arxiv.org/abs/2403.06738), Chen et al., Arxiv 2024 248 | - [Envision3D: One Image to 3D with Anchor Views Interpolation](http://arxiv.org/abs/2403.08902), Pang et al., Arxiv 2024 249 | - [Make-Your-3D: Fast and Consistent Subject-Driven 3D Content Generation](http://arxiv.org/abs/2403.09625), Liu et al., Arxiv 2024 250 | - [Controllable Text-to-3D Generation via Surface-Aligned Gaussian Splatting](https://arxiv.org/abs/2403.09981), Li et al., Arxiv 2024 251 | - [FDGaussian: Fast Gaussian Splatting from Single Image via Geometric-aware Diffusion Model](https://arxiv.org/abs/2403.10242), Feng et al., Arxiv 2024 252 | - [Isotropic3D: Image-to-3D Generation Based on a Single CLIP Embedding](https://arxiv.org/abs/2403.10395), Liu et al., Arxiv 2024 253 | - [SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image using Latent Video Diffusion](https://arxiv.org/abs/2403.12008), Vikram et al., Arxiv 2024 254 | - [Generic 3D Diffusion Adapter Using Controlled Multi-View Editing](https://arxiv.org/abs/2403.12032), Chen et al., Arxiv 2024 255 | - [VFusion3D: Learning Scalable 3D Generative Models from Video Diffusion Models](https://arxiv.org/abs/2403.12034), Han et al., Arxiv 2024 256 | - [Garment3DGen: 3D Garment Stylization and Texture Generation](http://arxiv.org/abs/2403.19655), Zhang et al., Arxiv 2024 257 | - [MVD-Fusion: Single-view 3D via Depth-consistent Multi-view Generation](https://arxiv.org/abs/2404.03656), Hu et al., CVPR 2024 258 | - [Magic-Boost: Boost 3D Generation with Mutli-View Conditioned Diffusion](https://arxiv.org/abs/2404.06429), Yang et al., Arxiv 2024 259 | - [InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models](http://arxiv.org/abs/2404.07191), Xu et al., Arxiv 2024 260 | - [Magic-Boost: Boost 3D Generation with Mutli-View Conditioned Diffusion](https://arxiv.org/abs/2404.06429), Yang et al., Arxiv 2024 261 | - [Grounded Compositional and Diverse Text-to-3D with Pretrained Multi-View Diffusion Model](https://arxiv.org/pdf/2404.18065), Li et al., Arxiv 2024 262 | - [Multi-view Image Prompted Multi-view Diffusion for Improved 3D Generation](https://arxiv.org/abs/2404.17419), Kim et al., Arxiv 2024 263 | - [MVDiff: Scalable and Flexible Multi-View Diffusion for 3D Object Reconstruction from Single-View](https://arxiv.org/pdf/2405.03894), Bourigault et al., Arxiv 2024 264 | - [CAT3D: Create Anything in 3D with Multi-View Diffusion Models](http://arxiv.org/abs/2405.10314), Gao et al., Arxiv 2024 265 | - [CraftsMan: High-fidelity Mesh Generation with 3D Native Generation and Interactive Geometry Refiner](https://arxiv.org/abs/2405.14979), Li et al., Arxiv 2024 266 | - [Ouroboros3D: Image-to-3D Generation via 3D-aware Recursive Diffusion](https://arxiv.org/pdf/2406.03184), Wen et al., Arxiv 2024 267 | - [Cycle3D: High-quality and Consistent Image-to-3D Generation via Generation-Reconstruction Cycle](https://arxiv.org/abs/2407.19548), Tang et al., Arxiv 2024 268 | 269 | ## 2D Diffusion without Pretraining 270 | ### 3D Objects 271 | - [Novel View Synthesis with Diffusion Models](https://arxiv.org/abs/2210.04628), Watson et al., ICLR 2023 272 | - [Generative Novel View Synthesis with 3D-Aware Diffusion Models](https://arxiv.org/abs/2304.02602), Chan et al., Arxiv 2023 273 | - [NerfDiff: Single-image View Synthesis with NeRF-guided Distillation from 3D-aware Diffusion](https://arxiv.org/abs/2302.10109), Gu et al., ICML 2023 274 | - [3DDesigner: Towards Photorealistic 3D Object Generation and Editing with Text-guided Diffusion Models](https://arxiv.org/abs/2211.14108), Li et al., Arxiv 2022 275 | - [SparseFusSparseFusion: Distilling View-conditioned Diffusion for 3D Reconstruction](https://arxiv.org/abs/2212.00792), Zhou and Tulsiani, CVPR 2023 276 | - [HoloDiffusion: Training a 3D Diffusion Model using 2D Images](https://arxiv.org/abs/2303.16509), Karnewar et al., CVPR 2023 277 | - [Renderdiffusion: Image Diffusion for 3D Reconstruction, Inpainting and Generation](https://arxiv.org/abs/2211.09869), Anciukevičius et al., CVPR 2023 278 | - [Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct Supervision](https://arxiv.org/abs/2306.11719), Tewari et al., Arxiv 2023 279 | - [3D-aware Image Generation using 2D Diffusion Models](https://arxiv.org/abs/2303.17905), Xiang et al., Arxiv 2023 280 | - [Viewset Viewset Diffusion: (0-)Image-Conditioned 3D Generative Models from 2D Data](https://arxiv.org/abs/2306.07881), Szymanowicz et al., Arxiv 2023 281 | - [HOLOFUSION: Towards Photo-realistic 3D Generative Modeling](https://arxiv.org/pdf/2308.14244.pdf), Karnewar et al., Arxiv 2023 282 | - [ZeroNVS: Zero-Shot 360-Degree View Synthesis from a Single Real Image](http://arxiv.org/abs/2310.17994), Sargent et al., Arxiv 2023 283 | - [Instant3D: Fast Text-to-3D with Sparse-view Generation and Large Reconstruction Model](https://arxiv.org/abs/2311.06214), Li et al., Arxiv 2023 284 | - [DMV3D: Denoising Multi-View Diffusion using 3D Large Reconstruction Model](https://arxiv.org/abs/2311.09217), Xu et al., Arxiv 2023 285 | - [LRM: Large Reconstruction Model for Single Image to 3D](https://arxiv.org/abs/2311.04400), Hong et al., Arxiv 2023 286 | - [WildFusion: Learning 3D-Aware Latent Diffusion Models in View Space](https://arxiv.org/abs/2311.13570), Schwarz et al., Arxiv 2023 287 | - [ViewFusion: Learning Composable Diffusion Models for Novel View Synthesis](https://arxiv.org/abs/2402.02906), Spiegl et al., Arxiv 2024 288 | 289 | ### 3D Scenes 290 | - [Consistent View Synthesis with Pose-Guided Diffusion Models](https://arxiv.org/abs/2303.17598), Tseng et al., CVPR 2023 291 | - [Long-Term Photometric Consistent Novel View Synthesis with Diffusion Models](https://arxiv.org/abs/2304.10700), Yu et al., Arxiv 2023 292 | - [DiffDreamer: Towards Consistent Unsupervised Single-view Scene Extrapolation with Conditional Diffusion Models](https://arxiv.org/abs/2211.12131), Cai et al., Arxiv 2023 293 | - [SemCity: Semantic Scene Generation with Triplane Diffusion](https://sglab.kaist.ac.kr/SemCity/), Lee et al., CVPR 2024 294 | 295 | ## Diffusion in 3D Space 296 | ### 3D Gaussians 297 | - [GVGEN: Text-to-3D Generation with Volumetric Representation](https://arxiv.org/abs/2403.12957), He et al., Arxiv 2024 298 | - [GaussianCube: Structuring Gaussian Splatting using Optimal Transport for 3D Generative Modeling](http://arxiv.org/abs/2403.19655), Zhang et al., Arxiv 2024 299 | - [Atlas Gaussians Diffusion for 3D Generation with Infinite Number of Points](https://arxiv.org/abs/2408.13055), Yang et al., Arxiv 2024 300 | - [DiffusionGS: Baking Gaussian Splatting into Diffusion Denoiser for Fast and Scalable Single-stage Image-to-3D Generation and Reconstruction](https://arxiv.org/abs/2411.14384), Cai et al., ICCV 2025 301 | 302 | ### Point Cloud, Meshs, Volumes 303 | 304 | - [Diffusion Probabilistic Models for 3D Point Cloud Generation](https://arxiv.org/pdf/2103.01458.pdf), Luo et al., CVPR 2021 305 | - [3d shape generation and completion through point-voxel diffusion](https://arxiv.org/pdf/2104.03670), Zhou et al., Arxiv 2021 306 | - [A Diffusion-ReFinement Model for Sketch-to-Point Modeling](https://link.springer.com/chapter/10.1007/978-3-031-26293-7_4), Kong et al., ACCV 2022 307 | - [Controllable Mesh Generation Through Sparse Latent Point Diffusion Models](http://arxiv.org/pdf/2303.07938), Lyu et al., CVPR 2023 308 | - [Point-E: A System for Generating 3D Point Clouds from Complex Prompts](https://arxiv.org/pdf/2212.08751), Nichol et al., ICML 2023 309 | - [DiffFacto: Controllable Part-Based 3D Point Cloud Generation with Cross Diffusion](https://arxiv.org/pdf/2305.01921.pdf), Nakayama et al., Arxiv 2023 310 | - [Sketch and Text Guided Diffusion Model for Colored Point Cloud Generation](https://arxiv.org/abs/2308.02874), Wu et al., ICCV 2023 311 | - [DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generation](https://arxiv.org/abs/2307.01831), Mo et al., Arxiv 2023 312 | - [MeshGPT: Generating Triangle Meshes with Decoder-Only Transformers](https://arxiv.org/abs/2311.15475), Siddiqui et al., CVPR 2024 313 | - [ShapeGPT: 3D Shape Generation with A Unified Multi-modal Language Model](http://arxiv.org/abs/2311.17618), Yin et al., Arxiv 2023 314 | - [VolumeDiffusion: Flexible Text-to-3D Generation with Efficient Volumetric Encoder](https://arxiv.org/abs/2312.11459), Tang et al., Arxiv 2023 315 | - [SPiC·E: Structural Priors in 3D Diffusion Models using Cross-Entity Attention](https://arxiv.org/abs/2311.17834), Sella et al., Arxiv 2023 316 | - [PolyDiff: Generating 3D Polygonal Meshes with Diffusion Models](https://arxiv.org/abs/2312.11417), Alliegro et al., Arxiv 2023 317 | 318 | 319 | ### Implicit Representation 320 | 321 | - [Learning A Diffusion Prior For Nerfs](https://arxiv.org/pdf/2304.14473.pdf), Yang et al., ICLRW 2023 322 | - [Tetrahedral Diffusion Models for 3D Shape Generation](https://arxiv.org/pdf/2211.13220.pdf), Nikolai and Torben et al., Arxiv 2022 323 | - [MeshDiffusion: Score-based Generative 3D Mesh Modeling](https://arxiv.org/pdf/2303.08133), Liu et al., ICLR 2023 324 | - [Neural Wavelet-domain Diffusion for 3D Shape Generation](https://arxiv.org/pdf/2209.08725), Hui et al., SIGGRAPH Asia 2022 325 | - [Neural Wavelet-domain Diffusion for 3D Shape Generation, Inversion, and Manipulation](https://arxiv.org/pdf/2302.00190), Hu and Hui et al., Arxiv 2023 326 | - [DiffRF: Rendering-Guided 3D Radiance Field Diffusion](https://arxiv.org/pdf/2212.01206.pdf), Muller et al., CVPR 2023 327 | - [Locally Attentional SDF Diffusion for Controllable 3D Shape Generation](http://arxiv.org/pdf/2305.04461), Zheng et al., SIGGRAPH 2023 328 | - [HyperDiffusion: Generating Implicit Neural Fields with Weight-Space Diffusion](https://arxiv.org/pdf/2303.17015), Erkoç et al., ICCV 2023 329 | - [DiffComplete: Diffusion-based Generative 3D Shape Completion](https://arxiv.org/pdf/2306.16329), Chu et al., Arxiv 2023 330 | - [DiffRoom: Diffusion-based High-Quality 3D Room Reconstruction and Generation](https://arxiv.org/pdf/2306.00519), Ju et al., Arxiv 2023 331 | - [Surf-D: High-Quality Surface Generation for Arbitrary Topologies using Diffusion Models](https://arxiv.org/abs/2311.17050), Yu et al., Arxiv 2023 332 | 333 | ### Triplane 334 | 335 | - [3D Neural Field Generation using Triplane Diffusion](https://arxiv.org/pdf/2211.16677.pdf), Shue et al., Arxiv 2022 336 | - [DiffusionSDF: Conditional Generative Modeling of Signed Distance Functions](https://arxiv.org/pdf/2211.13757), Chou et al., Arxiv 2022 337 | - [Rodin: A Generative Model for Sculpting 3D Digital Avatars Using Diffusion](https://arxiv.org/pdf/2212.06135.pdf), Wang et al., CVPR 2023 338 | - [3DGen: Triplane Latent Diffusion for Textured Mesh Generation](https://arxiv.org/pdf/2303.05371), Gupta et al., Arxiv 2023 339 | - [Single-Stage Diffusion NeRF: A Unified Approach to 3D Generation and Reconstruction](https://arxiv.org/pdf/2304.06714.pdf), Chen et al., Arxiv 2023 340 | - [Learning Controllable 3D Diffusion Models from Single-view Images](https://arxiv.org/pdf/2304.06700), Gu et al., Arxiv 2023 341 | - [Compress3D: a Compressed Latent Space for 3D Generation from a Single Image](https://arxiv.org/abs/2403.13524), Zhang et al., Arxiv 2024 342 | - [Dual3D: Efficient and Consistent Text-to-3D Generation with Dual-mode Multi-view Latent Diffusion](https://arxiv.org/pdf/2405.09874), Li et al., Arxiv 2024 343 | - [Direct3D: Scalable Image-to-3D Generation via 3D Latent Diffusion Transformer](https://arxiv.org/pdf/2405.14832), Wu et al., Arxiv 2024 344 | 345 | ### Latent Representation 346 | 347 | - [GAUDI: A Neural Architect for Immersive 3D Scene Generation](https://arxiv.org/pdf/2207.13751.pdf), Bautista et al., NeurIPS 2022 348 | - [LION: Latent Point Diffusion Models for 3D Shape Generation](https://arxiv.org/pdf/2210.06978.pdf), Zeng et al., NeurIPS 2022 349 | - [Diffusion-SDF: Text-to-Shape via Voxelized Diffusion](https://arxiv.org/pdf/2212.03293), Li et al., CVPR 2023 350 | - [3D-LDM: Neural Implicit 3D Shape Generation with Latent Diffusion Models](https://arxiv.org/pdf/2212.00842), Nam et al., Arxiv 2022 351 | - [3DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Models](http://arxiv.org/pdf/2301.11445), Zhang et al., SIGGRAPH 2023 352 | - [Shap-E: Generating Conditional 3D Implicit Functions](https://arxiv.org/pdf/2305.02463.pdf), Jun et al., Arxiv 2023 353 | - [StyleAvatar3D: Leveraging Image-Text Diffusion Models for High-Fidelity 3D Avatar Generation](https://arxiv.org/pdf/2305.19012), Zhang et al., Arxiv 2023 354 | - [AutoDecoding Latent 3D Diffusion Models](https://arxiv.org/pdf/2307.05445), Ntavelis et al., Arxiv 2023 355 | - [XCube: Large-Scale 3D Generative Modeling using Sparse Voxel Hierarchies](https://arxiv.org/abs/2312.03806), Ren et al., CVPR 2024 356 | - [LN3Diff: Scalable Latent Neural Fields Diffusion for Speedy 3D Generation](https://arxiv.org/abs/2403.12019), Lan et al., ECCV 2024 357 | - [GaussianAnything: Interactive Point Cloud Latent Diffusion for 3D Generation](https://arxiv.org/abs/2411.08033), Lan et al., Arxiv 2024 358 | - [Structured 3D Latents for Scalable and Versatile 3D Generation](https://trellis3d.github.io/), Xiang et al., Arxiv 2024 359 | 360 | ## Novel Representations 361 | - [X-Ray: A Sequential 3D Representation for Generation](https://arxiv.org/pdf/2404.14329), Hu et al., Arxiv 2024 362 | - [Geometry Image Diffusion: Fast and Data-Efficient Text-to-3D with Image-Based Surface Representation](https://arxiv.org/pdf/2409.03718), Elizarov et al., Arxiv 2024 363 | - [3DTopia-XL: Scaling High-quality 3D Asset Generation via Primitive Diffusion](https://arxiv.org/abs/2409.12957), Chen et al., Arxiv 2024 364 | - [An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion](https://arxiv.org/pdf/2408.03178), Yan et al., Arxiv 2024 365 | 366 | ## Diffusion for Motion 367 | 368 | ### Human Motion 369 | - [SMooDi: Stylized Motion Diffusion Model](https://arxiv.org/abs/2407.12783), Lei et al., ECCV 2024 370 | - [Single Motion Diffusion](https://sinmdm.github.io/SinMDM-page/), Raab et al., ICLR 2024 371 | - [OmniControl: Control Any Joint at Any Time for Human Motion Generation](https://arxiv.org/abs/2310.08580), Xie et al., ICLR 2024 372 | - [Human Motion Diffusion as a Generative Prior](https://priormdm.github.io/priorMDM-page/), Shafir et al., ICLR 2024 373 | - [MotionMix: Weakly-Supervised Diffusion for Controllable Motion Generation](https://arxiv.org/abs/2401.11115), Hoang et al., AAAI 2024 374 | - [DNO: Optimizing Diffusion Noise Can Serve As Universal Motion Priors](https://korrawe.github.io/dno-project/), Karunratanakul et al., Arxiv 2023 375 | - [RoHM: Robust Human Motion Reconstruction via Diffusion](https://arxiv.org/pdf/2401.08570.pdf), Zhang et al., Arxiv 2023 376 | - [EMDM: Efficient Motion Diffusion Model for Fast, High-Quality Human Motion Generation](https://frank-zy-dou.github.io/projects/EMDM/index.html), Zhou et al., Arxiv 2023 377 | - [DiffusionPhase: Motion Diffusion in Frequency Domain](https://arxiv.org/abs/2312.04036), Wan et al., Arxiv 2023 378 | - [InterControl: Generate Human Motion Interactions by Controlling Every Joint](https://arxiv.org/abs/2311.15864), Wang et al., Arxiv 2023 379 | - [AAMDM: Accelerated Auto-regressive Motion Diffusion Model](https://arxiv.org/abs/2401.06146), Li et al., Arxiv 2023 380 | - [ReMoS: Reactive 3D Motion Synthesis for Two-Person Interactions](https://vcai.mpi-inf.mpg.de/projects/remos/), Ghosh et al., Arxiv 2023 381 | - [HOI-Diff: Text-Driven Synthesis of 3D Human-Object Interactions using Diffusion Models](https://arxiv.org/abs/2312.06553), Peng et al., Arxiv 2023 382 | - [Controllable Motion Diffusion Model](https://arxiv.org/abs/2306.00416), Shi et al., Arxiv 2023 383 | - [MAS: Multi-view Ancestral Sampling for 3D motion generation using 2D diffusion](https://arxiv.org/abs/2310.14729), Kapon et al., Arxiv 2023 384 | - [CG-HOI: Contact-Guided 3D Human-Object Interaction Generation](https://arxiv.org/pdf/2311.16097.pdf), Diller et al., Arxiv 2023 385 | - [A Unified Framework for Multimodal, Multi-Part Human Motion Synthesis](https://arxiv.org/pdf/2311.16471.pdf), Zhou et al., Arxiv 2023 386 | - [Controllable Motion Synthesis and Reconstruction with Autoregressive Diffusion Models](https://arxiv.org/abs/2304.04681), Yin et al., Arxiv 2023 387 | - [Guided Motion Diffusion for Controllable Human Motion Synthesis](https://openaccess.thecvf.com/content/ICCV2023/papers/Karunratanakul_Guided_Motion_Diffusion_for_Controllable_Human_Motion_Synthesis_ICCV_2023_paper.pdf), Karunratanakul et al., ICCV 2023 388 | - 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