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News

  • 05/08/2023 The code and the pretrained model are released!
  • 23/10/2023 We have collected recent advances in Human Avatars in this repo, welcome stars!



TransHuman

Official code for ICCV 2023 paper:

TransHuman: A Transformer-based Human Representation for Generalizable Neural Human Rendering
Xiao Pan1,2, Zongxin Yang1, Jianxin Ma2, Chang Zhou2, Yi Yang1
1 ReLER Lab, CCAI, Zhejiang University; 2 Alibaba DAMO Academy

[Project Page | arXiv]

  • We present a brand-new framework named TransHuman for generalizable neural human rendering, which learns the painted SMPL under the canonical space and captures the global relationships between human parts with transformers.

  • We achieve SOTA performance on various settings and datasets.

  • We also have better efficiency.



Environment

We test with:

  • python==3.6.12
  • pytorch==1.10.2
  • cuda==11.3
# under TransHuman dir

# create conda environment
conda create -n transhuman python=3.6 
conda activate transhuman

# make sure that the pytorch cuda is consistent with the system cuda
# install pytorch via conda
https://pytorch.org/get-started/locally/

# install requirements 
pip install -r requirements.txt

# install pytorch3d (we build from source)
https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md



Dataset Preparation

ZJU-MoCap

Please follow NHP to prepare the ZJU-MoCap dataset.

The final structure of data folder should be:

# under TransHuman dir
-data
  -smplx
    - smpl
    - ...
  -zju_mocap
    - CoreView_313
    - ...
  -zju_rasterization
    - CoreView_313
    - ...

TODO: Code for more datasets are coming.



Training

sh ./scripts/train.sh

The checkpoints will be saved under ./data/trained_model/transhuman/$EXP_NAME .



Evaluation

sh ./scripts/test.sh $GPU_NUMBER $EPOCH_NUMBER $EXP_NAME

The config of different settings are provided in test.sh. Modify them as you need.

For reproducing the results in the paper, please download the official checkpoints from here.

Put it under ./data/trained_model/transhuman/official, and run:

sh ./scripts/test.sh 0 2100 official



Visualization

Free-viewpoint video

  1. Render the free-viewpoint frames via running:

    sh ./scripts/video.sh $GPU_NUMBER $EPOCH_NUMBER $EXP_NAME

    The rendered frames will be saved under ./data/perform/$EXP_NAME.

  2. Use gen_freeview_video.py for getting the final video.

Mech reconstruction

  1. Extract the mesh via running:

    sh ./script/mesh.sh $GPU_NUMBER $EPOCH_NUMBER $EXP_NAME

    The meshes will be saved under ./data/mesh/$EXP_NAME .

  2. Render the meshes using render_mesh_dynamic.py. The rendered frames will also be saved under ./data/mesh/$EXP_NAME

  3. Use gen_freeview_video.py for getting the final video.



Citation

If you find our work useful, please kindly cite:

@InProceedings{Pan_2023_ICCV,
    author    = {Pan, Xiao and Yang, Zongxin and Ma, Jianxin and Zhou, Chang and Yang, Yi},
    title     = {TransHuman: A Transformer-based Human Representation for Generalizable Neural Human Rendering},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2023},
    pages     = {3544-3555}
}



Contact

For questions, feel free to contact [email protected].



Acknowledgments

This project is mainly based on the code from NHP and humannerf. We also thank Sida Peng of Zhejiang University for helpful discussions on details of ZJU-MoCap dataset.