HuMMan v1.0: Reconstruction Subset

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HuMMan v1.0: Reconstruction Subset (HuMMan-Recon) consists of 153 subjects and 339 sequences. Color images, masks (via matting), SMPL parameters, and camera parameters are provided. It is a challenging dataset for its collection of diverse subject appearance and expressive actions. Moreover, it unleashes the potential to benchmark reconstruction algorithms under realistic settings with commercial sensors, dynamic subjects, and computer vision-powered automatic annotations. We also provide textured meshes reconstructed using a classical pipeline from multi-view RGB-D images.

Download links and a toolbox can be found here.

Please contact Zhongang Cai (caiz0023@e.ntu.edu.sg) for feedback or to add benchmarks.

Benchmark: Generalizable Animatable Avatar from Single Image

[1] Neural Human Performer: Learning Generalizable Radiance Fields for Human Performance Rendering
[2] MPS-NeRF: Generalizable 3D Human Rendering from Multiview Images
[3] SHERF: Generalizable Human NeRF from a Single Image

License

HuMMan is under S-Lab License v1.0.

Citation

@inproceedings{cai2022humman,
  title={{HuMMan}: Multi-modal 4d human dataset for versatile sensing and modeling},
  author={Cai, Zhongang and Ren, Daxuan and Zeng, Ailing and Lin, Zhengyu and Yu, Tao and Wang, Wenjia and Fan,
          Xiangyu and Gao, Yang and Yu, Yifan and Pan, Liang and Hong, Fangzhou and Zhang, Mingyuan and
          Loy, Chen Change and Yang, Lei and Liu, Ziwei},
  booktitle={17th European Conference on Computer Vision, Tel Aviv, Israel, October 23--27, 2022,
             Proceedings, Part VII},
  pages={557--577},
  year={2022},
  organization={Springer}
}