├── .gitignore ├── CODE_OF_CONDUCT.md ├── LICENSE ├── README.md ├── SECURITY.md ├── SUPPORT.md ├── docs ├── _config.yml ├── css │ └── styles.css ├── img │ ├── Microsoft-logo-only.svg │ ├── Microsoft-logo.svg │ ├── Microsoft-logo_rgb_c-gray.png │ ├── Microsoft-logo_rgb_c-wht.png │ ├── Microsoft-logo_rgb_wht.png │ ├── dataset_samples.jpg │ ├── favicon.ico │ ├── joints_uncertainty.png │ ├── plot_20_L_Wrist.png │ ├── plot_21_R_Wrist.png │ ├── plot_all_bold.png │ └── results.png └── index.html └── download_data.py /.gitignore: -------------------------------------------------------------------------------- 1 | ## Ignore Visual Studio temporary files, build results, and 2 | ## files generated by popular Visual Studio add-ons. 3 | ## 4 | ## Get latest from https://github.com/github/gitignore/blob/main/VisualStudio.gitignore 5 | 6 | # User-specific files 7 | *.rsuser 8 | *.suo 9 | *.user 10 | *.userosscache 11 | *.sln.docstates 12 | 13 | # User-specific files (MonoDevelop/Xamarin Studio) 14 | *.userprefs 15 | 16 | # Mono auto generated files 17 | mono_crash.* 18 | 19 | # Build results 20 | [Dd]ebug/ 21 | [Dd]ebugPublic/ 22 | [Rr]elease/ 23 | [Rr]eleases/ 24 | x64/ 25 | x86/ 26 | [Ww][Ii][Nn]32/ 27 | [Aa][Rr][Mm]/ 28 | [Aa][Rr][Mm]64/ 29 | bld/ 30 | [Bb]in/ 31 | [Oo]bj/ 32 | [Ll]og/ 33 | [Ll]ogs/ 34 | 35 | # Visual Studio 2015/2017 cache/options directory 36 | .vs/ 37 | # Uncomment if you have tasks that create the project's static files in wwwroot 38 | #wwwroot/ 39 | 40 | # Visual Studio 2017 auto generated files 41 | Generated\ Files/ 42 | 43 | # MSTest test Results 44 | [Tt]est[Rr]esult*/ 45 | [Bb]uild[Ll]og.* 46 | 47 | # NUnit 48 | *.VisualState.xml 49 | TestResult.xml 50 | nunit-*.xml 51 | 52 | # Build Results of an ATL Project 53 | [Dd]ebugPS/ 54 | [Rr]eleasePS/ 55 | dlldata.c 56 | 57 | # Benchmark Results 58 | BenchmarkDotNet.Artifacts/ 59 | 60 | # .NET Core 61 | project.lock.json 62 | project.fragment.lock.json 63 | artifacts/ 64 | 65 | # ASP.NET Scaffolding 66 | ScaffoldingReadMe.txt 67 | 68 | # StyleCop 69 | StyleCopReport.xml 70 | 71 | # Files built by Visual Studio 72 | *_i.c 73 | *_p.c 74 | *_h.h 75 | *.ilk 76 | *.meta 77 | *.obj 78 | *.iobj 79 | *.pch 80 | *.pdb 81 | *.ipdb 82 | *.pgc 83 | *.pgd 84 | *.rsp 85 | *.sbr 86 | *.tlb 87 | *.tli 88 | *.tlh 89 | *.tmp 90 | *.tmp_proj 91 | *_wpftmp.csproj 92 | *.log 93 | *.tlog 94 | *.vspscc 95 | *.vssscc 96 | .builds 97 | *.pidb 98 | *.svclog 99 | *.scc 100 | 101 | # Chutzpah Test files 102 | _Chutzpah* 103 | 104 | # Visual C++ cache files 105 | ipch/ 106 | *.aps 107 | *.ncb 108 | *.opendb 109 | *.opensdf 110 | *.sdf 111 | *.cachefile 112 | *.VC.db 113 | *.VC.VC.opendb 114 | 115 | # Visual Studio profiler 116 | *.psess 117 | *.vsp 118 | *.vspx 119 | *.sap 120 | 121 | # Visual Studio Trace Files 122 | *.e2e 123 | 124 | # TFS 2012 Local Workspace 125 | $tf/ 126 | 127 | # Guidance Automation Toolkit 128 | *.gpState 129 | 130 | # ReSharper is a .NET coding add-in 131 | _ReSharper*/ 132 | *.[Rr]e[Ss]harper 133 | *.DotSettings.user 134 | 135 | # TeamCity is a build add-in 136 | _TeamCity* 137 | 138 | # DotCover is a Code Coverage Tool 139 | *.dotCover 140 | 141 | # AxoCover is a Code Coverage Tool 142 | .axoCover/* 143 | !.axoCover/settings.json 144 | 145 | # Coverlet is a free, cross platform Code Coverage Tool 146 | coverage*.json 147 | coverage*.xml 148 | coverage*.info 149 | 150 | # Visual Studio code coverage results 151 | *.coverage 152 | *.coveragexml 153 | 154 | # NCrunch 155 | _NCrunch_* 156 | .*crunch*.local.xml 157 | nCrunchTemp_* 158 | 159 | # MightyMoose 160 | *.mm.* 161 | AutoTest.Net/ 162 | 163 | # Web workbench (sass) 164 | .sass-cache/ 165 | 166 | # Installshield output folder 167 | [Ee]xpress/ 168 | 169 | # DocProject is a documentation generator add-in 170 | DocProject/buildhelp/ 171 | DocProject/Help/*.HxT 172 | DocProject/Help/*.HxC 173 | DocProject/Help/*.hhc 174 | DocProject/Help/*.hhk 175 | DocProject/Help/*.hhp 176 | DocProject/Help/Html2 177 | DocProject/Help/html 178 | 179 | # Click-Once directory 180 | publish/ 181 | 182 | # Publish Web Output 183 | *.[Pp]ublish.xml 184 | *.azurePubxml 185 | # Note: Comment the next line if you want to checkin your web deploy settings, 186 | # but database connection strings (with potential passwords) will be unencrypted 187 | *.pubxml 188 | *.publishproj 189 | 190 | # Microsoft Azure Web App publish settings. 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Capitalized terms are defined in Section 5. Data Provider and you agree as follows: 4 | 5 | 1. Provision of the Data 6 | 1.1. You may use, modify, and distribute the Data made available to you by the Data Provider under this R-UDA for Research Use if you follow the R-UDA’s terms. 7 | 1.2. Data Provider will not sue you or any Downstream Recipient for any claim arising out of the use, modification, or distribution of the Data provided you meet the terms of the R-UDA. 8 | 1.3. This R-UDA does not restrict your use, modification, or distribution of any portions of the Data that are in the public domain or that may be used, modified, or distributed under any other legal exception or limitation. 9 | 10 | 2. Restrictions 11 | 2.1. You agree that you will use the Data solely for Computational Use for non-commercial research. This restriction means that you may engage in non-commercial research activities (including non-commercial research undertaken by or funded via a commercial entity), but you may not use the Data or any Results in any commercial offering, including as part of a product or service (or to improve any product or service) you use or provide to others. 12 | 2.2. You may not receive money or other consideration in exchange for use or redistribution of Data. 13 | 14 | 3. Redistribution of Data 15 | 3.1. You may redistribute the Data, so long as: 16 | 3.1.1. You include with any Data you redistribute all credit or attribution information that you received with the Data, and your terms require any Downstream Recipient to do the same; and 17 | 3.1.2. You bind each recipient to whom you redistribute the Data to the terms of the R-UDA. 18 | 19 | 4. No Warranty, Limitation of Liability 20 | 4.1. Data Provider does not represent or warrant that it has any rights whatsoever in the Data. 21 | 4.2. THE DATA IS PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OR CONDITIONS OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. 22 | 4.3. NEITHER DATA PROVIDER NOR ANY UPSTREAM DATA PROVIDER SHALL HAVE ANY LIABILITY FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING WITHOUT LIMITATION LOST PROFITS), HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE DATA OR RESULTS, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. 23 | 24 | 5. Definitions 25 | 5.1. “Computational Use” means activities necessary to enable the use of Data (alone or along with other material) for analysis by a computer. 26 | 5.2. “Data” means the material you receive under the R-UDA in modified or unmodified form, but not including Results. 27 | 5.3. “Data Provider” means the source from which you receive the Data and with whom you enter into the R-UDA. 28 | 5.4. “Downstream Recipient” means any person or persons who receives the Data directly or indirectly from you in accordance with the R-UDA. 29 | 5.5. “Result” means anything that you develop or improve from your use of Data that does not include more than a de minimis portion of the Data on which the use is based. Results may include de minimis portions of the Data necessary to report on or explain use that has been conducted with the Data, such as figures in scientific papers, but do not include more. Artificial intelligence models trained on Data (and which do not include more than a de minimis portion of Data) are Results. 30 | 5.6. “Upstream Data Providers” means the source or sources from which the Data Provider directly or indirectly received, under the terms of the R-UDA, material that is included in the Data. 31 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ![SynthEgo dataset](docs/img/dataset_samples.jpg) 2 | 3 | # The SynthEgo Dataset 4 | 5 | The SynthEgo dataset was introduced in our paper [**SimpleEgo: Predicting probabilistic body pose from egocentric cameras**](https://microsoft.github.io/SimpleEgo/). 6 | 7 | The dataset contains: 8 | 9 | - 60,000 stereo pair synthetic RGB images from a head mounted camera at 1280x720 pixel resolution. 10 | - [SMPL-H](https://mano.is.tue.mpg.de/) pose and identity parameters for each stereo pair. 11 | - 3D joint locations in world and camera space for each image. 12 | - 2D joint locations in image space for each image. 13 | - Camera parameters for each image. 14 | 15 | ## Downloading the dataset 16 | 17 | The license terms for the [MANO](https://mano.is.tue.mpg.de/) dataset and parts of the [AMASS](https://amass.is.tue.mpg.de/) dataset prevent redistribution. 18 | As such, we include names and indices for these poses in the dataset we distribute, but not the pose parameters themselves. 19 | **For simplicity, we provide a script to download the MANO, AMASS and SynthEgo datasets and splice in the relevant pose parameters automatically.** 20 | You will need to sign up for MANO and AMASS and provide the relevant credentials when prompted by the script. 21 | The only requirements for the script are numpy and wget, otherwise simply run `python download_dataset.py` from the command line. 22 | 23 | You can download the dataset in parts (3GB each) directly from the following links, though *this is not recommended*: 24 | 25 | - [Part 1](https://facesyntheticspubwedata.z6.web.core.windows.net/3dv-2024/synth_ego_01.zip) 26 | - [Part 2](https://facesyntheticspubwedata.z6.web.core.windows.net/3dv-2024/synth_ego_02.zip) 27 | - [Part 3](https://facesyntheticspubwedata.z6.web.core.windows.net/3dv-2024/synth_ego_03.zip) 28 | - [Part 4](https://facesyntheticspubwedata.z6.web.core.windows.net/3dv-2024/synth_ego_04.zip) 29 | - [Part 5](https://facesyntheticspubwedata.z6.web.core.windows.net/3dv-2024/synth_ego_05.zip) 30 | - [Part 6](https://facesyntheticspubwedata.z6.web.core.windows.net/3dv-2024/synth_ego_06.zip) 31 | - [Part 7](https://facesyntheticspubwedata.z6.web.core.windows.net/3dv-2024/synth_ego_07.zip) 32 | - [Part 7](https://facesyntheticspubwedata.z6.web.core.windows.net/3dv-2024/synth_ego_08.zip) 33 | - [Part 9](https://facesyntheticspubwedata.z6.web.core.windows.net/3dv-2024/synth_ego_09.zip) 34 | - [Part 10](https://facesyntheticspubwedata.z6.web.core.windows.net/3dv-2024/synth_ego_10.zip) 35 | 36 | ## Dataset layout 37 | 38 | Once extracted, the dataset contains files for each sample of the form: 39 | 40 | - `metadata_0000000_0000.json` 41 | - `img_L_0000000_0000.jpg` 42 | - `img_R_0000000_0000.jpg` 43 | 44 | Where the first number indicates the subject index from 0 to 6000, and the second number indicates the frame index from 0 to 10. 45 | The metadata files are structured as follows: 46 | 47 | ```json 48 | { 49 | "pose": [ "52x3 array of SMPL-H thetas/pose parameters" ], 50 | "translation": [ "3 element SMPL-H translation vector" ], 51 | "identity": [ "10 element SMPL-H neutral beta vector" ], 52 | "cameras": { 53 | "camera_L": { 54 | "world_to_camera": [ "4x4 extrinsic matrix" ], 55 | "camera_to_image": [ "3x3 intrinsic matrix" ], 56 | "resolution": [1280, 720] 57 | }, 58 | "camera_R": { "as for camera_L" }, 59 | }, 60 | "landmarks": { 61 | "3D_world": [ "54x3 joint locations in world space" ], 62 | "3D_camera_L": [ "54x3 joint locations in camera space for camera_L" ], 63 | "2D_camera_L": [ "54x2 joint locations in image space for camera_L" ], 64 | "3D_camera_R": [ "54x3 joint locations in camera space for camera_R" ], 65 | "2D_camera_R": [ "54x2 joint locations in image space for camera_R" ], 66 | } 67 | } 68 | ``` 69 | 70 | ## Citation 71 | 72 | If you use the SynthEgo Dataset your research, please cite the following paper: 73 | 74 | ```bibtex 75 | @inproceedings{cuevas2024simpleego, 76 | title={{SimpleEgo}: Predicting probabilistic body pose from egocentric cameras}, 77 | author={Cuevas-Velasquez, Hanz and Hewitt, Charlie and Aliakbarian, Sadegh and Baltru{\v{s}}aitis, Tadas}, 78 | booktitle={2024 International Conference on 3D Vision (3DV)}, 79 | pages={1446--1455}, 80 | year={2024}, 81 | organization={IEEE} 82 | } 83 | ``` 84 | -------------------------------------------------------------------------------- /SECURITY.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | ## Security 4 | 5 | Microsoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet) and [Xamarin](https://github.com/xamarin). 6 | 7 | If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://aka.ms/security.md/definition), please report it to us as described below. 8 | 9 | ## Reporting Security Issues 10 | 11 | **Please do not report security vulnerabilities through public GitHub issues.** 12 | 13 | Instead, please report them to the Microsoft Security Response Center (MSRC) at [https://msrc.microsoft.com/create-report](https://aka.ms/security.md/msrc/create-report). 14 | 15 | If you prefer to submit without logging in, send email to [secure@microsoft.com](mailto:secure@microsoft.com). If possible, encrypt your message with our PGP key; please download it from the [Microsoft Security Response Center PGP Key page](https://aka.ms/security.md/msrc/pgp). 16 | 17 | You should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Additional information can be found at [microsoft.com/msrc](https://www.microsoft.com/msrc). 18 | 19 | Please include the requested information listed below (as much as you can provide) to help us better understand the nature and scope of the possible issue: 20 | 21 | * Type of issue (e.g. buffer overflow, SQL injection, cross-site scripting, etc.) 22 | * Full paths of source file(s) related to the manifestation of the issue 23 | * The location of the affected source code (tag/branch/commit or direct URL) 24 | * Any special configuration required to reproduce the issue 25 | * Step-by-step instructions to reproduce the issue 26 | * Proof-of-concept or exploit code (if possible) 27 | * Impact of the issue, including how an attacker might exploit the issue 28 | 29 | This information will help us triage your report more quickly. 30 | 31 | If you are reporting for a bug bounty, more complete reports can contribute to a higher bounty award. Please visit our [Microsoft Bug Bounty Program](https://aka.ms/security.md/msrc/bounty) page for more details about our active programs. 32 | 33 | ## Preferred Languages 34 | 35 | We prefer all communications to be in English. 36 | 37 | ## Policy 38 | 39 | Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://aka.ms/security.md/cvd). 40 | 41 | 42 | -------------------------------------------------------------------------------- /SUPPORT.md: -------------------------------------------------------------------------------- 1 | # TODO: The maintainer of this repo has not yet edited this file 2 | 3 | **REPO OWNER**: Do you want Customer Service & Support (CSS) support for this product/project? 4 | 5 | - **No CSS support:** Fill out this template with information about how to file issues and get help. 6 | - **Yes CSS support:** Fill out an intake form at [aka.ms/onboardsupport](https://aka.ms/onboardsupport). 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WHERE WILL YOU HELP PEOPLE?**. 22 | 23 | ## Microsoft Support Policy 24 | 25 | Support for this **PROJECT or PRODUCT** is limited to the resources listed above. 26 | -------------------------------------------------------------------------------- /docs/_config.yml: -------------------------------------------------------------------------------- 1 | theme: jekyll-theme-cayman -------------------------------------------------------------------------------- /docs/css/styles.css: -------------------------------------------------------------------------------- 1 | @import "https://cdn.jsdelivr.net/npm/bulma@0.9.3/css/bulma.min.css"; 2 | @import "https://cdn.jsdelivr.net/npm/@creativebulma/bulma-tooltip@1.2.0/dist/bulma-tooltip.min.css"; 3 | @media screen and (min-width: 1024px) { 4 | .container { 5 | max-width: 860px; 6 | } 7 | } 8 | 9 | .authors>span { 10 | padding: 0 0.5rem; 11 | display: inline-block; 12 | } 13 | 14 | @media only screen and (max-width: 480px) { 15 | a.button.is-rounded.is-link.is-light:not(:last-child) { 16 | margin-bottom: 0.75em; 17 | } 18 | } 19 | 20 | .footer-links a { 21 | color: inherit !important; 22 | padding: 0 1em; 23 | } -------------------------------------------------------------------------------- /docs/img/Microsoft-logo-only.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 5 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | -------------------------------------------------------------------------------- /docs/img/Microsoft-logo.svg: -------------------------------------------------------------------------------- 1 | 2 | 3 | 5 | 12 | 13 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | -------------------------------------------------------------------------------- /docs/img/Microsoft-logo_rgb_c-gray.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/SimpleEgo/c5b171e5aaabad62ee1f22dbe81f0c9de49168d9/docs/img/Microsoft-logo_rgb_c-gray.png -------------------------------------------------------------------------------- /docs/img/Microsoft-logo_rgb_c-wht.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/SimpleEgo/c5b171e5aaabad62ee1f22dbe81f0c9de49168d9/docs/img/Microsoft-logo_rgb_c-wht.png -------------------------------------------------------------------------------- /docs/img/Microsoft-logo_rgb_wht.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/SimpleEgo/c5b171e5aaabad62ee1f22dbe81f0c9de49168d9/docs/img/Microsoft-logo_rgb_wht.png -------------------------------------------------------------------------------- /docs/img/dataset_samples.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/SimpleEgo/c5b171e5aaabad62ee1f22dbe81f0c9de49168d9/docs/img/dataset_samples.jpg -------------------------------------------------------------------------------- /docs/img/favicon.ico: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/SimpleEgo/c5b171e5aaabad62ee1f22dbe81f0c9de49168d9/docs/img/favicon.ico -------------------------------------------------------------------------------- /docs/img/joints_uncertainty.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/SimpleEgo/c5b171e5aaabad62ee1f22dbe81f0c9de49168d9/docs/img/joints_uncertainty.png -------------------------------------------------------------------------------- /docs/img/plot_20_L_Wrist.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/SimpleEgo/c5b171e5aaabad62ee1f22dbe81f0c9de49168d9/docs/img/plot_20_L_Wrist.png -------------------------------------------------------------------------------- /docs/img/plot_21_R_Wrist.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/SimpleEgo/c5b171e5aaabad62ee1f22dbe81f0c9de49168d9/docs/img/plot_21_R_Wrist.png -------------------------------------------------------------------------------- /docs/img/plot_all_bold.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/SimpleEgo/c5b171e5aaabad62ee1f22dbe81f0c9de49168d9/docs/img/plot_all_bold.png -------------------------------------------------------------------------------- /docs/img/results.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/microsoft/SimpleEgo/c5b171e5aaabad62ee1f22dbe81f0c9de49168d9/docs/img/results.png -------------------------------------------------------------------------------- /docs/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | SimpleEgo 8 | 9 | 10 | 11 | 12 | 13 | 15 | 22 | 23 | 24 | 25 | 57 |
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113 | Our work addresses the problem of egocentric human pose estimation from downwards-facing cameras on 114 | head-mounted devices (HMD). 115 | This presents a challenging scenario, as parts of the body often fall outside of the image or are 116 | occluded. 117 | Previous solutions minimize this problem by using fish-eye camera lenses to capture a wider view, 118 | but these can present hardware design issues. 119 | They also predict 2D heat-maps per joint and lift them to 3D space to deal with self-occlusions, but 120 | this requires large network architectures which are impractical to deploy on resource-constrained 121 | HMDs. 122 | We predict pose from images captured with conventional rectilinear camera lenses. 123 | This resolves hardware design issues, but means body parts are often out of frame. 124 | As such, we directly regress probabilistic joint rotations represented as matrix Fisher 125 | distributions for a parameterized body model. 126 | This allows us to quantify pose uncertainties and explain out-of-frame or occluded joints. 127 | This also removes the need to compute 2D heat-maps and allows for simplified DNN architectures which 128 | require less compute. 129 | Given the lack of egocentric datasets using rectilinear camera lenses, we introduce the 130 | SynthEgo dataset, a synthetic dataset with 60K stereo images containing high diversity of 131 | pose, shape, clothing and skin tone. 132 | Our approach achieves state-of-the-art results for this challenging configuration, reducing mean 133 | per-joint position error by 23% overall and 58% for the lower body. 134 | Our architecture also has eight times fewer parameters and runs twice as fast as the current 135 | state-of-the-art. 136 | Experiments show that training on our synthetic dataset leads to good generalization to real world 137 | images without fine-tuning. 138 |

139 |
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141 |
142 | 143 | 144 |
145 |
146 |

147 | SynthEgo Dataset 148 |

149 | 150 |
151 |

152 | To construct the SynthEgo dataset we render 60K stereo pairs at 1280×720 pixel 153 | resolution, building on the pipeline of Hewitt et al. 154 | This dataset is comprised of 6000 unique identities, each performing 10 different poses in 10 155 | different lighting environments. 156 | Each identity is made up of a randomly sampled body shape, skin textures sampled from a library of 157 | 25 and randomly recolored, and clothing assets sampled from a library of 202. 158 | Lighting environments are sampled from a library of 489 HDRIs, to ensure correct disparity of the 159 | environment between the stereo pair, we project the HDRI background onto the ground plane. 160 | Poses are sampled from a library of over 2 million unique poses and randomly mirrored; sampling is 161 | weighted by the mean absolute joint angle and common poses like T-pose are significantly 162 | down-weighted to increase diversity. 163 |

164 | 165 | 166 | 167 | 168 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | 178 | 179 | 180 | 181 | 182 | 183 | 184 | 185 | 186 | 187 | 188 | 189 | 190 | 191 | 192 | 193 | 194 | 195 | 196 | 197 | 198 | 199 | 200 | 201 | 202 | 203 | 204 | 205 | 206 | 207 | 208 | 209 | 210 | 211 | 212 | 213 | 214 | 215 | 216 | 217 | 218 | 219 | 220 | 221 | 222 | 223 | 224 | 225 | 226 | 227 | 228 | 229 | 230 | 231 | 232 | 233 | 234 | 235 | 236 | 237 | 238 | 239 | 240 |
Mo2Cap2 170 | xR-EgoPoseUnrealEgoSynthEgo
Unique Identities70046176000
EnvironmentsUnspecifiedUnspecified14489
Body ModelSMPLUnspecifiedUnrealEngineSMPL-H
Lens TypeFisheyeFisheyeFisheyeRectilinear
Mono/StereoMonoMonoStereoStereo
Body Shape GT✓
Joint Location GT✓✓✓✓
Joint Rotation GT✓
RealismLowMediumHighHigh
241 | 242 |

243 | We position the camera on the front of the forehead looking down at the body. 244 | The camera uses a pinhole model approximating the ZED 245 | mini stereo. 246 | We add uniform noise within ±1 cm to the location and ±10° around all axes of 247 | rotation of the camera to simulate misplacement and movement of the HMD on the head. 248 | The resulting images are typically quite challenging for pose estimation, as many parts of the body 249 | are often not seen by the camera. 250 |

251 |
252 |
253 |
254 |
255 |
256 |

257 | Method 258 |

259 |
260 |

261 | The goal of our method is to estimate the probability distribution over joint rotations $\mathbf{R} 262 | = \{\mathbf{R}_i\}^{N}_{i=1}$ conditioned on input image data $\mathbf{X}$, 263 | $p(\mathbf{R}|\mathbf{X})$. 264 | Following Sengupta 265 | et al, we train a neural network to regress Fisher parameters $\mathbf{F} = 266 | \{\mathbf{F}_i\}^{N}_{i=1}$ given input image data $\mathbf{X}$. 267 | From these predicted parameters we can calculate the expected rotation, $\mathbf{\hat{R}}_i$ and the 268 | concentration parameters for each joint $i$, $\kappa_{i,j}$. 269 | The latter describes the uncertainty of the rotation distribution. 270 |

271 | 272 |

273 | We train the neural network by minimizing loss $\mathcal{L} = \mathcal{L}_{FNLL} + \mathcal{L}_J$. 274 | $\mathcal{L}_{FNLL}$ is the matrix Fisher negative log-likelihood, promoting accurate local joint 275 | rotations. 276 | $$ 277 | \begin{aligned} 278 | \mathcal{L}_{FNLL}&=\sum_{i=1}^{N}log(c(\mathbf{F}_i))-\text{tr}(\mathbf{F}_i^\top \mathbf{R}_i) 279 | \end{aligned} 280 | $$ 281 | $\mathcal{L}_J$ supervises the 3D joint positions regressed from the parametric body model, SMPL-H, 282 | with shape parameters $\boldsymbol\beta$ with joint regressor $\mathcal{J}$. 283 | $$ 284 | \begin{aligned} 285 | J_{3D}(\mathbf{R},\boldsymbol\beta)=&\mathcal{J}(\textit{SMPL-H}(\mathbf{R}, \boldsymbol\beta))\\ 286 | \mathcal{L}_{J}=&\left \| J_{3D}(\hat{\mathbf{R}},\boldsymbol\beta)- 287 | J_{3D}(\mathbf{R},\boldsymbol\beta) \right \|^2_2 288 | \end{aligned} 289 | $$ 290 | This causes the network to consider the effect of the predicted rotations on the final pose, as the 291 | positions of child joints are influenced by the rotation of their parents in the kinematic tree of 292 | our body model. 293 |

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299 |

300 | Results 301 |

302 | 303 |
304 | 305 | 306 | 307 | 308 | 309 | 310 | 311 | 312 | 313 | 314 | 315 | 316 | 317 | 318 | 319 | 320 | 321 | 322 | 323 | 324 | 325 | 326 | 327 | 328 | 329 | 330 | 331 | 332 | 333 | 334 | 335 | 336 | 337 | 338 | 339 | 340 | 341 | 342 | 343 | 344 | 345 | 346 | 347 | 348 | 349 | 350 |
InputMethodPA-MPJPE (mm)
Upper BodyLower BodyHandsAll
MonocularxR-EgoPose50.1876.76127.3497.48
Ours38.4862.3598.9476.05
StereoUnrealEgo48.0677.06117.8591.67
Ours34.0054.5987.7867.31
351 |
352 | 353 |
354 |
355 |

356 | To evaluate the performance on real-world data, we recorded a dataset of 8378 stereo pair images 357 | from 11 different subjects performing actions like squatting, sitting, stretching, crossing 358 | arms, and interacting with small objects. 359 | Overall, our stereo network has the best performance. 360 | We observe that the extra information provided by the right image helps the network to better 361 | predict extremities. 362 | We also note that UnrealEgo and xR-EgoPose perform particularity poorly for lower body joints. 363 | This may be caused by the fact that the legs are not always visible, and that 2D heat-maps 364 | cannot provide uncertainties for joints outside of the image frame. 365 |

366 |
367 |
368 | Qualitative results on real data 369 |

Qualitative results of our method compared to recent work for synthetic and real data.

370 |
371 |
372 | 373 |
374 |
375 | Joint concentration parameter visualization 376 |

Axis specific concentration for different joints. Concentration is lowest around the primary axis of rotation for a given joint.

377 |
378 |
379 |

380 | Our paper demonstrates that the predicted uncertainty estimates capture extra information and 381 | priors about body pose, and shows empirically that the estimated uncertainties are reliable. 382 | While the former allows us to better explain the prediction of the model, the latter is of 383 | significant importance when it comes to deployment of our method in downstream tasks such as 384 | avatar animation, where uncertainty estimates can be used as a measure of reliance of the 385 | predicted poses. 386 |

387 |
388 |
389 | 390 |
391 |
392 | All Joints 393 | Correlation between uncertainty and error for all joints 394 |
395 |
396 | Left Wrist 397 | Correlation between uncertainty and error for the left wrist 398 |
399 |
400 | Right Wrist 401 | Correlation between uncertainty and error for the right wrist 402 |
403 |
404 |
405 |

Correlation of confidence with error; the higher the confidence the lower the error. Our confidence estimates are therefore reliable for downstream use.

406 |
407 |
408 |
409 |
410 |
411 |

412 | BibTeX 413 |

414 | 
415 | @inproceedings{cuevas2024simpleego,
416 |   title={{SimpleEgo}: Predicting probabilistic body pose from egocentric cameras},
417 |   author={Cuevas-Velasquez, Hanz and Hewitt, Charlie and Aliakbarian, Sadegh and Baltru{\v{s}}aitis, Tadas},
418 |   booktitle={2024 International Conference on 3D Vision (3DV)},
419 |   pages={1446--1455},
420 |   year={2024},
421 |   organization={IEEE}
422 | }
423 |
424 |
425 |
426 | 442 | 443 | 444 | 470 | 471 | 472 | -------------------------------------------------------------------------------- /download_data.py: -------------------------------------------------------------------------------- 1 | """Download and prepare the SynthEgo dataset. 2 | 3 | MANO and AMASS parts from MPII licenses prohibit redistribution, so this script downloads from the 4 | official source and splices in the correct data. 5 | """ 6 | import subprocess 7 | import sys 8 | import json 9 | from typing import Optional 10 | from pathlib import Path 11 | from getpass import getpass 12 | from zipfile import ZipFile 13 | import tarfile 14 | 15 | import numpy as np 16 | 17 | MANO_N_J = 15 18 | SMPL_H_N_J = 22 19 | LEFT_HAND = SMPL_H_N_J 20 | RIGHT_HAND = SMPL_H_N_J + MANO_N_J 21 | 22 | MANO_FILENAME = "manoposesv10" 23 | MOSH_FILENAME = "MoSh" 24 | POSELIM_FILENAME = "PosePrior" 25 | SYNTHEGO_DIRNAME = "SynthEgo" 26 | 27 | 28 | def _download_mpii_file( 29 | username: str, password: str, domain: str, file: str, out_path: Path 30 | ) -> None: 31 | out_path.parent.mkdir(exist_ok=True, parents=True) 32 | url = f"https://download.is.tue.mpg.de/download.php?domain={domain}&resume=1&sfile={file}" 33 | try: 34 | subprocess.check_call( 35 | [ 36 | "wget", 37 | "--post-data", 38 | f"username={username}&password={password}", 39 | url, 40 | "-O", 41 | out_path.as_posix(), 42 | "--no-check-certificate", 43 | "--continue", 44 | ] 45 | ) 46 | except FileNotFoundError as exc: 47 | raise RuntimeError("wget not found, please install it") from exc 48 | except subprocess.CalledProcessError: 49 | print("Download failed, check your login details") 50 | if out_path.exists(): 51 | out_path.unlink() 52 | sys.exit(1) 53 | 54 | 55 | def get_mano(out_dir: Path) -> None: 56 | """Download MANO data.""" 57 | print("Downloading MANO...") 58 | username = input("Username: ") 59 | password = getpass("Password: ") 60 | _download_mpii_file( 61 | username, 62 | password, 63 | "mano", 64 | f"{MANO_FILENAME}.zip", 65 | out_dir / f"{MANO_FILENAME}.zip", 66 | ) 67 | 68 | 69 | def get_amass(out_dir: Path) -> None: 70 | """Download AMASS data.""" 71 | print("Downloading AMASS...") 72 | username = input("Username: ") 73 | password = getpass("Password: ") 74 | _download_mpii_file( 75 | username, 76 | password, 77 | "amass", 78 | f"amass_per_dataset/smplh/gender_specific/mosh_results/{MOSH_FILENAME}.tar.bz2", 79 | out_dir / f"{MOSH_FILENAME}.tar.bz2", 80 | ) 81 | _download_mpii_file( 82 | username, 83 | password, 84 | "amass", 85 | f"amass_per_dataset/smplh/gender_specific/mosh_results/{POSELIM_FILENAME}.tar.bz2", 86 | out_dir / f"{POSELIM_FILENAME}.tar.bz2", 87 | ) 88 | 89 | 90 | def extract(data_path: Path, out_path: Optional[Path] = None) -> None: 91 | """Extract the data from the given path.""" 92 | print(f"Extracting {data_path.name}...") 93 | if data_path.suffix == ".zip": 94 | out_path = out_path or data_path.parent / data_path.stem 95 | with ZipFile(data_path) as f: 96 | f.extractall(out_path) 97 | elif data_path.suffix == ".bz2": 98 | out_path = out_path or data_path.parent / data_path.name.replace(".tar.bz2", "") 99 | with tarfile.open(data_path, "r:bz2") as f: 100 | f.extractall(out_path) 101 | else: 102 | raise ValueError(f"Unknown file type {data_path.suffix}") 103 | 104 | 105 | def download_synthego(out_dir: Path) -> None: 106 | """Download the SynthEgo dataset.""" 107 | out_dir.mkdir(exist_ok=True, parents=True) 108 | for part in range(1, 11): 109 | out_path = out_dir / f"synth_ego_{part:02d}.zip" 110 | print(f"Downloading SynthEgo part {part}...") 111 | url = f"https://facesyntheticspubwedata.z6.web.core.windows.net/3dv-2024/synth_ego_{part:02d}.zip" 112 | try: 113 | subprocess.check_call( 114 | [ 115 | "wget", 116 | url, 117 | "-O", 118 | str(out_path), 119 | "--no-check-certificate", 120 | "--continue", 121 | ] 122 | ) 123 | except FileNotFoundError as exc: 124 | raise RuntimeError("wget not found, please install it") from exc 125 | except subprocess.CalledProcessError: 126 | print("Download failed") 127 | if out_path.exists(): 128 | out_path.unlink() 129 | sys.exit(1) 130 | 131 | 132 | def process_metadata(data_dir: Path) -> None: 133 | """Process the metadata to include the correct pose data.""" 134 | # load MANO dataset 135 | mano_left = np.load(data_dir / f"{MANO_FILENAME}/mano_poses_v1_0/handsOnly_REGISTRATIONS_r_lm___POSES___L.npy") 136 | mano_right = np.load(data_dir / f"{MANO_FILENAME}/mano_poses_v1_0/handsOnly_REGISTRATIONS_r_lm___POSES___R.npy") 137 | # fill in the data 138 | for metadata_fn in (data_dir / SYNTHEGO_DIRNAME).glob("*.json"): 139 | with open(metadata_fn, "r") as f: 140 | metadata = json.load(f) 141 | if isinstance(metadata["pose"][1], str): 142 | # body pose comes from AMASS 143 | seq_name: str = metadata["pose"][1] 144 | frame = int(seq_name.split("_")[-2]) 145 | mirrored = seq_name.split("_")[-1] == 1 146 | assert not mirrored 147 | seq_path = Path("/".join(seq_name.split("/")[1:])).with_suffix(".npz").as_posix() 148 | if seq_name.startswith("MoSh_MPI_MoSh"): 149 | # fix paths to match downloaded data 150 | seq_path = seq_path.replace("Data/moshpp_fits_SMPL", "MPI_mosh") 151 | seq_path = seq_path.replace(".npz", "_poses.npz") 152 | if not (data_dir / MOSH_FILENAME / seq_path).exists(): 153 | # there is a sequence incorrectly named with _poses_poses 154 | seq_path = seq_path.replace(".npz", "_poses.npz") 155 | seq_data = np.load(data_dir / MOSH_FILENAME / seq_path) 156 | elif seq_name.startswith("MoSh_MPI_PoseLimits"): 157 | # fix paths to match downloaded data 158 | seq_path = seq_path.replace("Data/moshpp_fits_SMPL", "MPI_Limits") 159 | seq_path = seq_path.replace(".npz", "_poses.npz") 160 | seq_data = np.load(data_dir / POSELIM_FILENAME / seq_path) 161 | else: 162 | raise RuntimeError(f"Unknown sequence name {seq_name}") 163 | # we resampled to ~30 fps so have to adjust the frame number 164 | frame_step = int(np.floor(seq_data["mocap_framerate"] / 30)) 165 | seq = seq_data["poses"][::frame_step] 166 | # exclude root joint 167 | metadata["pose"][1:SMPL_H_N_J] = ( 168 | seq[frame].reshape((-1, 3))[1:SMPL_H_N_J].tolist() 169 | ) 170 | if isinstance(metadata["pose"][LEFT_HAND], str): 171 | # left hand comes from MANO 172 | idx = int(metadata["pose"][LEFT_HAND].split("_")[1]) 173 | metadata["pose"][LEFT_HAND:RIGHT_HAND] = ( 174 | mano_left[idx].reshape((MANO_N_J, 3)).tolist() 175 | ) 176 | if isinstance(metadata["pose"][RIGHT_HAND], str): 177 | # right hand comes from MANO 178 | idx = int(metadata["pose"][RIGHT_HAND].split("_")[1]) 179 | metadata["pose"][RIGHT_HAND:] = ( 180 | mano_right[idx].reshape((MANO_N_J, 3)).tolist() 181 | ) 182 | with open(metadata_fn, "w") as f: 183 | json.dump(metadata, f, indent=4) 184 | 185 | 186 | def main() -> None: 187 | """Download and process the dataset.""" 188 | assert len(sys.argv) == 2, "Usage: python process_pose_gt.py " 189 | data_dir = Path(sys.argv[1]) 190 | # download data from MPII sources 191 | get_amass(data_dir) 192 | get_mano(data_dir) 193 | # extract the data 194 | for path in list(data_dir.glob("*.zip")) + list(data_dir.glob("*.bz2")): 195 | extract(path) 196 | path.unlink() 197 | # download the SynthEgo dataset 198 | zip_dir = data_dir / f"{SYNTHEGO_DIRNAME}_zip" 199 | download_synthego(zip_dir) 200 | # extract the SynthEgo dataset 201 | for path in list(zip_dir.glob("*.zip")): 202 | extract(path, data_dir / SYNTHEGO_DIRNAME) 203 | path.unlink() 204 | zip_dir.rmdir() 205 | # process the metadata 206 | process_metadata(data_dir) 207 | 208 | 209 | if __name__ == "__main__": 210 | main() 211 | --------------------------------------------------------------------------------