EasyMocap

EasyMocap is an open-source toolbox for markerless human motion capture from RGB videos. In this project, we provide a lot of motion capture demos in different settings.

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Core features

Multiple views of single person

This is the basic code for fitting SMPL[1]/SMPL+H[2]/SMPL-X[3] model to capture body+hand+face poses from multiple views.

^{Videos are from ZJU-MoCap, with 23 calibrated and synchronized cameras.}

Internet video with a mirror

^{This video is from Youtube.}

Multiple Internet videos with a specific action (Coming soon)

Multiple views of multiple people (Comming soon)

Others

This project is used by many other projects:

• [CVPR21] Dense Reconstruction and View Synthesis from Sparse Views

Other features

• Camera calibration
• Pose guided synchronization
• Annotator
• Exporting of multiple data formats(bvh, asf/amc, ...)

Updates

• 04/02/2021: We are now rebuilding our project for v0.2, please stay tuned. v0.1 is available at this link.

Installation

See doc/install for more instructions.

Quick Start

See doc/quickstart for more instructions.

Not Quick Start

See doc/notquickstart for more instructions.

Evaluation

The weight parameters can be set according your data.

More quantitative reports will be added in doc/evaluation.md

Acknowledgements

Here are the great works this project is built upon:

• SMPL models and layer are from MPII SMPL-X model.
• Some functions are borrowed from SPIN, VIBE, SMPLify-X
• The method for fitting 3D skeleton and SMPL model is similar to TotalCapture, without using point cloud.

We also would like to thank Wenduo Feng who is the performer in the sample data.

Contact

Please open an issue if you have any questions. We appreciate all contributions to improve our project.

Citation

This project is a part of our work iMocap, Mirrored-Human and Neural Body

Please consider citing these works if you find this repo is useful for your projects.

@inproceedings{dong2020motion,
  title={Motion capture from internet videos},
  author={Dong, Junting and Shuai, Qing and Zhang, Yuanqing and Liu, Xian and Zhou, Xiaowei and Bao, Hujun},
  booktitle={European Conference on Computer Vision},
  pages={210--227},
  year={2020},
  organization={Springer}
}

@inproceedings{peng2021neural,
  title={Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans},
  author={Peng, Sida and Zhang, Yuanqing and Xu, Yinghao and Wang, Qianqian and Shuai, Qing and Bao, Hujun and Zhou, Xiaowei},
  booktitle={CVPR},
  year={2021}
}

@inproceedings{fang2021mirrored,
  title={Reconstructing 3D Human Pose by Watching Humans in the Mirror},
  author={Fang, Qi and Shuai, Qing and Dong, Junting and Bao, Hujun and Zhou, Xiaowei},
  booktitle={CVPR},
  year={2021}
}

Reference

[1] Loper, Matthew, et al. "SMPL: A skinned multi-person linear model." ACM transactions on graphics (TOG) 34.6 (2015): 1-16.
[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.
[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.
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.
[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)