209 lines
8.8 KiB
Markdown
209 lines
8.8 KiB
Markdown
<!--
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* @Date: 2021-01-13 20:32:12
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* @Author: Qing Shuai
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* @LastEditors: Qing Shuai
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* @LastEditTime: 2021-01-24 22:11:37
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* @FilePath: /EasyMocapRelease/Readme.md
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-->
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# EasyMocap
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**EasyMocap** is an open-source toolbox for **markerless human motion capture** from RGB videos.
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In this project, we provide the basic code for fitting SMPL[1]/SMPL+H[2]/SMPLX[3] model to capture body+hand+face poses from multiple views.
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|Input|:heavy_check_mark: Skeleton|:heavy_check_mark: SMPL|
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|----|----|----|
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> We plan to intergrate more interesting algorithms, please stay tuned!
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1. [Multi-Person from Multiple Views](https://github.com/zju3dv/mvpose)
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2. [Mocap from Multiple **Uncalibrated** and **Unsynchronized** Videos](https://arxiv.org/pdf/2008.07931.pdf)
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3. [Dense Reconstruction and View Synthesis from **Sparse Views**](https://zju3dv.github.io/neuralbody/)
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## Installation
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### 1. Download SMPL models
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This step is the same as [smplx](https://github.com/vchoutas/smplx#model-loading).
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To download the *SMPL* model go to [this](http://smpl.is.tue.mpg.de) (male and female models, version 1.0.0, 10 shape PCs) and [this](http://smplify.is.tue.mpg.de) (gender neutral model) project website and register to get access to the downloads section.
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To download the *SMPL+H* model go to [this project website](http://mano.is.tue.mpg.de) and register to get access to the downloads section.
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To download the *SMPL-X* model go to [this project website](https://smpl-x.is.tue.mpg.de) and register to get access to the downloads section.
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**Place them as following:**
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```bash
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data
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└── smplx
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├── J_regressor_body25.npy
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├── J_regressor_body25_smplh.txt
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├── J_regressor_body25_smplx.txt
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├── smpl
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│ ├── SMPL_FEMALE.pkl
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│ ├── SMPL_MALE.pkl
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│ └── SMPL_NEUTRAL.pkl
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├── smplh
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│ ├── MANO_LEFT.pkl
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│ ├── MANO_RIGHT.pkl
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│ ├── SMPLH_female.pkl
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│ ├── SMPLH_FEMALE.pkl
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│ ├── SMPLH_male.pkl
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│ └── SMPLH_MALE.pkl
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└── smplx
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├── SMPLX_FEMALE.pkl
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├── SMPLX_MALE.pkl
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└── SMPLX_NEUTRAL.pkl
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```
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### 2. Requirements
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- torch==1.4.0
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- torchvision==0.5.0
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- opencv-python
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- [pyrender](https://pyrender.readthedocs.io/en/latest/install/index.html#python-installation): for visualization
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- chumpy: for loading SMPL model
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- OpenPose[4]: for 2D pose
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Some of python libraries can be found in `requirements.txt`. You can test different version of PyTorch.
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## Quick Start
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We provide an example multiview dataset[[dropbox](https://www.dropbox.com/s/24mb7r921b1g9a7/zju-ls-feng.zip?dl=0)][[BaiduDisk](https://pan.baidu.com/s/1lvAopzYGCic3nauoQXjbPw)(vg1z)], which has 800 frames from 23 synchronized and calibrated cameras. After downloading the dataset, you can run the following example scripts.
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```bash
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data=path/to/data
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out=path/to/output
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# 0. extract the video to images
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python3 scripts/preprocess/extract_video.py ${data}
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# 1. example for skeleton reconstruction
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python3 code/demo_mv1pmf_skel.py ${data} --out ${out} --vis_det --vis_repro --undis --sub_vis 1 7 13 19
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# 2. example for SMPL reconstruction
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python3 code/demo_mv1pmf_smpl.py ${data} --out ${out} --end 300 --vis_smpl --undis --sub_vis 1 7 13 19 --gender male
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# 2. example for SMPL-X reconstruction
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python3 code/demo_mv1pmf_smpl.py ${data} --out ${out} --undis --body bodyhandface --sub_vis 1 7 13 19 --start 400 --model smplx --vis_smpl --gender male
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```
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## Not Quick Start
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### 0. Prepare Your Own Dataset
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```bash
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zju-ls-feng
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├── intri.yml
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├── extri.yml
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└── videos
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├── 1.mp4
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├── 2.mp4
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├── ...
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├── 8.mp4
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└── 9.mp4
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```
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The input videos are placed in `videos/`.
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Here `intri.yml` and `extri.yml` store the camera intrinsici and extrinsic parameters. For example, if the name of a video is `1.mp4`, then there must exist `K_1`, `dist_1` in `intri.yml`, and `R_1((3, 1), rotation vector of camera)`, `T_1(3, 1)` in `extri.yml`. The file format is following [OpenCV format](https://docs.opencv.org/master/dd/d74/tutorial_file_input_output_with_xml_yml.html).
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### 1. Run [OpenPose](https://github.com/CMU-Perceptual-Computing-Lab/openpose)
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```bash
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data=path/to/data
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out=path/to/output
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python3 scripts/preprocess/extract_video.py ${data} --openpose <openpose_path> --handface
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```
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- `--openpose`: specify the openpose path
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- `--handface`: detect hands and face keypoints
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### 2. Run the code
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```bash
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# 1. example for skeleton reconstruction
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python3 code/demo_mv1pmf_skel.py ${data} --out ${out} --vis_det --vis_repro --undis --sub_vis 1 7 13 19
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# 2. example for SMPL reconstruction
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python3 code/demo_mv1pmf_smpl.py ${data} --out ${out} --end 300 --vis_smpl --undis --sub_vis 1 7 13 19
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```
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The input flags:
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- `--undis`: use to undistort the images
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- `--start, --end`: control the begin and end number of frames.
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The output flags:
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- `--vis_det`: visualize the detection
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- `--vis_repro`: visualize the reprojection
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- `--sub_vis`: use to specify the views to visualize. If not set, the code will use all views
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- `--vis_smpl`: use to render the SMPL mesh to images.
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### 3. Output
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The results are saved in `json` format.
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```bash
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<output_root>
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├── keypoints3d
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│ ├── 000000.json
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│ └── xxxxxx.json
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└── smpl
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├── 000000.jpg
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├── 000000.json
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└── 000004.json
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```
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The data in `keypoints3d/000000.json` is a list, each element represents a human body.
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```bash
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{
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'id': <id>,
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'keypoints3d': [[x0, y0, z0, c0], [x1, y1, z0, c1], ..., [xn, yn, zn, cn]]
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}
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```
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The data in `smpl/000000.json` is also a list, each element represents the SMPL parameters which is slightly different from official model.
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```bash
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{
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"id": <id>,
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"Rh": <(1, 3)>,
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"Th": <(1, 3)>,
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"poses": <(1, 72/78/87)>,
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"expression": <(1, 10)>,
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"shapes": <(1, 10)>
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}
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```
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We set the first 3 dimensions of `poses` to zero, and add a new parameter `Rh` to represents the global oritentation, the vertices of SMPL model V = RX(theta, beta) + T.
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If you use SMPL+H model, the poses contains `22x3+6+6`. We use `6` pca coefficients for each hand. `3(jaw, left eye, right eye)x3` poses of head are added for SMPL-X model.
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## Evaluation
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In our code, we do not set the best weight parameters, you can adjust these according your data. If you find a set of good weights, feel free to tell me.
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We will add more quantitative reports in [doc/evaluation.md](doc/evaluation.md)
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## Acknowledgements
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Here are the great works this project is built upon:
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- SMPL models and layer are from MPII [SMPL-X model](https://github.com/vchoutas/smplx).
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- Some functions are borrowed from [SPIN](https://github.com/nkolot/SPIN), [VIBE](https://github.com/mkocabas/VIBE), [SMPLify-X](https://github.com/vchoutas/smplify-x)
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- The method for fitting 3D skeleton and SMPL model is similar to [TotalCapture](http://www.cs.cmu.edu/~hanbyulj/totalcapture/), without using point cloud.
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We also would like to thank Wenduo Feng who is the performer in the sample data.
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## Contact
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Please open an issue if you have any questions.
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## Citation
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This project is a part of our work [iMocap](https://zju3dv.github.io/iMoCap/) and [Neural Body](https://zju3dv.github.io/neuralbody/)
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Please consider citing these works if you find this repo is useful for your projects.
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```bibtex
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@inproceedings{dong2020motion,
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title={Motion capture from internet videos},
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author={Dong, Junting and Shuai, Qing and Zhang, Yuanqing and Liu, Xian and Zhou, Xiaowei and Bao, Hujun},
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booktitle={European Conference on Computer Vision},
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pages={210--227},
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year={2020},
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organization={Springer}
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}
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@article{peng2020neural,
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title={Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans},
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author={Peng, Sida and Zhang, Yuanqing and Xu, Yinghao and Wang, Qianqian and Shuai, Qing and Bao, Hujun and Zhou, Xiaowei},
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journal={arXiv preprint arXiv:2012.15838},
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year={2020}
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}
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```
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## Reference
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```bash
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[1] Loper, Matthew, et al. "SMPL: A skinned multi-person linear model." ACM transactions on graphics (TOG) 34.6 (2015): 1-16.
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[2] Romero, Javier, Dimitrios Tzionas, and Michael J. Black. "Embodied hands: Modeling and capturing hands and bodies together." ACM Transactions on Graphics (ToG) 36.6 (2017): 1-17.
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[3] Pavlakos, Georgios, et al. "Expressive body capture: 3d hands, face, and body from a single image." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019.
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Bogo, Federica, et al. "Keep it SMPL: Automatic estimation of 3D human pose and shape from a single image." European conference on computer vision. Springer, Cham, 2016.
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[4] Cao, Z., Hidalgo, G., Simon, T., Wei, S.E., Sheikh, Y.: Openpose: real-time multi-person 2d pose estimation using part affinity fields. arXiv preprint arXiv:1812.08008 (2018)
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``` |