Surface-Based Motion Modeling for Dynamic Human Rendering

We propose SurMo, a new paradigm for learning dynamic human rendering from videos by jointly modeling the temporal motion dynamics and human appearances in a unified framework based on a novel surface-based triplane. We extend the existing well-adopted paradigm of "Pose Encoding → Appearance Decoding" to "Motion Encoding → Physical Motion Decoding, Appearance Decoding".

This repository contains the code of SurMo that is built upon HVTR and HVTR++.

SurMo: Surface-based 4D Motion Modeling for Dynamic Human Rendering
Tao Hu, Fangzhou Hong, Ziwei Liu
CVPR 2024 [Project Page] [Video] [Paper]

HVTR++: Image and Pose Driven Human Avatars using Hybrid Volumetric-Textural Rendering
Tao Hu, Hongyi Xu, Linjie Luo, Tao Yu, Zerong Zheng, He Zhang, Yebin Liu, Matthias Zwicker
TVCG 2023 [Project Page] [Video] [Paper]

HVTR: Hybrid Volumetric-Textural Rendering for Human Avatars
Tao Hu, Tao Yu, Zerong Zheng, He Zhang, Yebin Liu, Matthias Zwicker
3DV 2022 [Project Page] [Video] [Paper]

Instructions

Test Results

To facilitate comparisons with our model in subsequent work, we have saved our rendering results on ZJU-MoCap OneDrive

Installation

NVIDIA GPUs are required for this project. We have trained and tested code on NVIDIA V100. We recommend using anaconda to manage the python environments.

conda create --name surmo python=3.9
conda install pytorch==1.10.1 torchvision==0.11.2 cudatoolkit=11.1 -c pytorch
conda install -c fvcore -c iopath -c conda-forge fvcore iopath
conda install pytorch3d -c pytorch3d
pip install -r requirements.txt

Inference

Download Models & Assets & Datasets

Download the pre-trained models and assets. Put them in DATA_DIR/result/trained_model and DATA_DIR/asset respectively, or download models via scripts python download_models.py. DATA_DIR is specified as ../data in default.

Download ZJU-MoCap dataset and put it in the folder zju_mocap (e.g., DATA_DIR/zju_mocap/CoreView_3XX)

Register and download SMPL models here. Put them in the folder smpl_data.

The folder structure should look like

DATA_DIR
├── zju_mocap
└── asset/
    ├── smpl_data/
        └── SMPL_NEUTRAL.pkl
├── result/trained_model

Commands

Inference script for models (313, 315, 377, 386, 387, 394) trained on ZJU-MoCap.

bash scripts/3XX_inference.sh gpu_ids

e.g., bash scripts/313_inference.sh 0

The inference results will be found in DATA_DIR/result/.

Training

Commands

Training script for subjects (313, 315, 377, 386, 387, 394) on ZJU-MoCap.

bash scripts/3XX_train.sh gpu_ids

e.g., bash scripts/313_train.sh 0,1,2,3

The trained models will be found in DATA_DIR/result/trained_model/.

License

Distributed under the S-Lab License. See LICENSE for more information.

Citation

  @misc{hu2024surmo,
      title={SurMo: Surface-based 4D Motion Modeling for Dynamic Human Rendering}, 
      author={Tao Hu and Fangzhou Hong and Ziwei Liu},
      year={2024},
      eprint={2404.01225},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
  }

  @ARTICLE{hu2023hvtrpp,
      author={Hu, Tao and Xu, Hongyi and Luo, Linjie and Yu, Tao and Zheng, Zerong and Zhang, He and Liu, Yebin and Zwicker, Matthias},
      journal={IEEE Transactions on Visualization and Computer Graphics}, 
      title={HVTR++: Image and Pose Driven Human Avatars using Hybrid Volumetric-Textural Rendering}, 
      year={2023}
  }

  @inproceedings{hu2022hvtr,
      title={HVTR: Hybrid Volumetric-Textural Rendering for Human Avatars},
      author={Hu, Tao and Yu, Tao and Zheng, Zerong and Zhang, He and Liu, Yebin and Zwicker, Matthias},
      booktitle = {2022 International Conference on 3D Vision (3DV)},
      year = {2022}
}