Editable Free-viewpoint Video Using a Layered Neural Representation

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

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_taekwondo.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_walking.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.

 @inproceedings{zhang2021stnerf,
                title={Editable Free-Viewpoint Video using a Layered Neural Representation},
                author={Jiakai, Zhang
                        and Xinhang, Liu
                        and Xinyi, Ye
                        and Fuqiang, Zhao
                        and Yanshun, Zhang
                        and Minye, Wu
                        and Yingliang, Zhang
                        and Lan, Xu
                        and Jingyi, Yu
                        },
                year={2021},
                booktitle={ACM SIGGRAPH},
               }