Awesome

st-nerf

We provide PyTorch implementations for our paper: Editable Free-viewpoint Video Using a Layered Neural Representation

SIGGRAPH 2021

Jiakai Zhang, Xinhang Liu, Xinyi Ye, Fuqiang Zhao, Yanshun Zhang, Minye Wu, Yingliang Zhang, Lan Xu and Jingyi Yu

st-nerf: Project | Paper

Getting Started

Installation

• Clone this repo:

git clone https://github.com/DarlingHang/st-nerf
cd st-nerf

• Install PyTorch and other dependencies using:

conda create -n st-nerf python=3.8.5
conda activate st-nerf    
conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=11.0 -c pytorch
conda install imageio matplotlib
pip install yacs kornia robpy

Datasets

The walking and taekwondo datasets can be downloaded from here.

Apply a pre-trained model to render demo videos

• We provide our pretrained models which can be found under the outputs folder.
• We provide some example scripts under the demo folder.
• To run our demo scripts, you need to first downloaded the corresponding dataset, and put them under the folder specified by DATASETS -> TRAIN in configs/config_taekwondo.yml and configs/config_walking.yml
• For the walking sequence, you can render videos where some performers are hided by typing the command:

python demo/walking_demo.py -c configs/config_walking.yml

• For the taekwondo sequence, you can render videos where performers are translated and scaled by typing the command:

python demo/taekwondo_demo.py -c configs/config_taekwondo.yml

• The rendered images and videos will be under outputs/taekwondo/rendered and outputs/walking/rendered

Acknowlegements

We borrowed some codes from Multi-view Neural Human Rendering (NHR).

Citation

If you use this code for your research, please cite our papers.

@article{zhang2021editable,
  title={Editable free-viewpoint video using a layered neural representation},
  author={Zhang, Jiakai and Liu, Xinhang and Ye, Xinyi and Zhao, Fuqiang and Zhang, Yanshun and Wu, Minye and Zhang, Yingliang and Xu, Lan and Yu, Jingyi},
  journal={ACM Transactions on Graphics (TOG)},
  volume={40},
  number={4},
  pages={1--18},
  year={2021},
  publisher={ACM New York, NY, USA}
}