**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.
[](./doc/quickstart.md) [](https://colab.research.google.com/drive/1Cyvu_lPFUajr2RKt6yJIfS3HQIIYl6QU?usp=sharing)
This is the basic code for fitting SMPL[^loper2015]/SMPL+H[^romero2017]/SMPL-X[^pavlakos2019]/MANO[^romero2017] model to capture body+hand+face poses from multiple views.
With our proposed method, we release two large dataset of human motion: LightStage and Mirrored-Human. See the [website](https://chingswy.github.io/Dataset-Demo/) for more details.
If you would like to download the ZJU-Mocap dataset, please sign the [agreement](https://zjueducn-my.sharepoint.com/:b:/g/personal/pengsida_zju_edu_cn/EUPiybrcFeNEhdQROx4-LNEBm4lzLxDwkk1SBcNWFgeplA?e=BGDiQh), and email it to Qing Shuai (s_q@zju.edu.cn) and cc Xiaowei Zhou (xwzhou@zju.edu.cn) to request the download link.
- 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)
- The method for fitting 3D skeleton and SMPL model is similar to [TotalCapture](http://www.cs.cmu.edu/~hanbyulj/totalcapture/), without using point clouds.
- We integrate some easy-to-use functions for previous great work:
EasyMocap is **built by** researchers from the 3D vision group of Zhejiang University: [**Qing Shuai**](https://chingswy.github.io/), [**Qi Fang**](https://raypine.github.io/), [**Junting Dong**](https://jtdong.com/), [**Sida Peng**](https://pengsida.net/), **Di Huang**, [**Hujun Bao**](http://www.cad.zju.edu.cn/home/bao/), **and** [**Xiaowei Zhou**](https://xzhou.me/).
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](https://github.com/zju3dv/EasyMocap/graphs/contributors).
This project is a part of our work [iMocap](https://zju3dv.github.io/iMoCap/), [Mirrored-Human](https://zju3dv.github.io/Mirrored-Human/), [mvpose](https://zju3dv.github.io/mvpose/) and [Neural Body](https://zju3dv.github.io/neuralbody/)
[^loper2015]: Loper, Matthew, et al. "SMPL: A skinned multi-person linear model." ACM transactions on graphics (TOG) 34.6 (2015): 1-16.
[^romero2017]: 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.
[^pavlakos2019]: 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.
<!-- [4] 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. -->
[^kolotouros2019]: Kolotouros, Nikos, et al. "Learning to reconstruct 3D human pose and shape via model-fitting in the loop." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019
[^bochkovskiy2020]: Bochkovskiy, Alexey, Chien-Yao Wang, and Hong-Yuan Mark Liao. "Yolov4: Optimal speed and accuracy of object detection." arXiv preprint arXiv:2004.10934 (2020).
<!-- [8] Sun, Ke, et al. "Deep high-resolution representation learning for human pose estimation." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019. -->
