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

• 🎉 Our SIGGRAPH 2022 Novel View Synthesis of Human Interactions From Sparse Multi-view Videos is released! Check the documentation.
• 🎉 EasyMocap v0.2 is released! We support motion capture from Internet videos. Please check the Quick Start for more details.

This is the basic code for fitting SMPL¹/SMPL+H²/SMPL-X³/MANO² model to capture body+hand+face poses from multiple views.

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

^{Captured with 8 cameras.}

This part is the basic code for fitting SMPL¹ with 2D keypoints estimation⁴⁵ and CNN initialization⁶.

^{The raw video is from Youtube.}

^{The raw video is from Youtube.}

^{Internet videos of Roger Federer's serving}

^{Captured with 8 consumer cameras}

^{Novel view synthesis for chanllenge motion(coming soon)}

^{Novel view synthesis for human interaction}

With our proposed method, we release two large dataset of human motion: LightStage and Mirrored-Human. See the website for more details.

If you would like to download the ZJU-Mocap dataset, please sign the agreement, and email it to Qing Shuai ([email protected]) and cc Xiaowei Zhou ([email protected]) to request the download link.

^{LightStage: captured with LightStage system}

^{Mirrored-Human: collected from the Internet}

Many works have achieved wonderful results based on our dataset:

• Real-time volumetric rendering of dynamic humans
• CVPR2022: HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video
• ECCV2022: KeypointNeRF: Generalizing Image-based Volumetric Avatars using Relative Spatial Encoding of Keypoints
• SIGGRAPH 2022 paper Drivable Volumetric Avatars using Texel-Aligned Features

^{Calibration for intrinsic and extrinsic parameters}

^{Annotator for bounding box, keypoints and mask}

• 11/03/2022: Support MultiNeuralBody.
• 12/25/2021: Support mediapipe keypoints detector.
• 08/09/2021: Add a colab demo here.
• 06/28/2021: The Multi-view Multi-person part is released!
• 06/10/2021: The real-time 3D visualization part is released!
• 04/11/2021: The calibration tool and the annotator are released.
• 04/11/2021: Mirrored-Human part is released.

See documentation for more instructions.

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 SMPLify-X(with 3D keypoints loss), TotalCapture(without using point clouds).
• We integrate some easy-to-use functions for previous great work:
  • easymocap/estimator/mediapipe_wrapper.py: MediaPipe
  • easymocap/estimator/SPIN : an SMPL estimator⁴
  • easymocap/estimator/YOLOv4: an object detector⁶
  • easymocap/estimator/HRNet : a 2D human pose estimator⁷

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

EasyMocap is built by researchers from the 3D vision group of Zhejiang University: Qing Shuai, Qi Fang, Junting Dong, Sida Peng, Di Huang, Hujun Bao, and Xiaowei Zhou.

We would like to thank Wenduo Feng, Di Huang, Yuji Chen, Hao Xu, Qing Shuai, Qi Fang, Ting Xie, Junting Dong, Sida Peng and Xiaopeng Ji who are the performers in the sample data. We would also like to thank all the people who has helped EasyMocap in any way.

This project is a part of our work iMocap, Mirrored-Human, mvpose, Neural Body, MultiNeuralBody, enerf.

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

@Misc{easymocap,  
    title = {EasyMoCap - Make human motion capture easier.},
    howpublished = {Github},  
    year = {2021},
    url = {https://github.com/zju3dv/EasyMocap}
}

@inproceedings{shuai2022multinb,
  title={Novel View Synthesis of Human Interactions from Sparse
Multi-view Videos},
  author={Shuai, Qing and Geng, Chen and Fang, Qi and Peng, Sida and Shen, Wenhao and Zhou, Xiaowei and Bao, Hujun},
  booktitle={SIGGRAPH Conference Proceedings},
  year={2022}
}

@inproceedings{lin2022efficient,
  title={Efficient Neural Radiance Fields for Interactive Free-viewpoint Video},
  author={Lin, Haotong and Peng, Sida and Xu, Zhen and Yan, Yunzhi and Shuai, Qing and Bao, Hujun and Zhou, Xiaowei},
  booktitle={SIGGRAPH Asia Conference Proceedings},
  year={2022}
}

@inproceedings{dong2021fast,
  title={Fast and Robust Multi-Person 3D Pose Estimation and Tracking from Multiple Views},
  author={Dong, Junting and Fang, Qi and Jiang, Wen and Yang, Yurou and Bao, Hujun and Zhou, Xiaowei},
  booktitle={T-PAMI},
  year={2021}
}
    
@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}
}