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RelightableAvatar (AAAI'2024)

Project Page | Paper | Video

Installation

Environment Setup

This repository has been tested on the Python 3.8, Pytorch 1.10.1 with CUDA 11.3, Ubuntu 22.04. We use a GTX 3090 for training and inference, please make sure enough GPU memory if using other cards.

conda env create -f environment.yml
conda activate RAvatar

It is recommended to build pytorch3d from source.

wget -O pytorch3d-0.4.0.zip https://github.com/facebookresearch/pytorch3d/archive/refs/tags/v0.4.0.zip
unzip pytorch3d-0.4.0.zip
cd pytorch3d-0.4.0 && python setup.py install && cd ..

SMPL Setup

Download smpl model from SMPL website, we use the neutral model from SMPL_python_v.1.1.0, and put the model files (basicmodel_f_lbs_10_207_0_v1.1.0.pkl, basicmodel_m_lbs_10_207_0_v1.1.0.pkl and basicmodel_neutral_lbs_10_207_0_v1.1.0.pkl) to $ROOT/data/smplx/smpl/.

Test with Trained Models

  • Download trained models from Google Drive, and put them to $ROOT/data/trained_model/.
  • Render subjects in novel light and novel poses.
python run_material.py --type visualize --cfg_file configs/material_ps_m3c.yaml exp_name material_ps_m3c novel_light True vis_pose_sequence True

Results are saved in $ROOT/data/. Target environment light and pose sequence can be set by 'novel_light_path' and 'novel_poses_path' parameter in the configuration.

Train from Scratch

Dataset preparation

  • People-Snapshot dataset

    1. Download the People-Snapshot dataset here.
    2. Create a soft link by: ln -s /path/to/people_snapshot ./data/people_snapshot
    3. Run this script to process the dataset: python process_snapshot.py

  • For ZJU-Mocap, Human3.6M and MonoCap dataset, we follow AnimatableNeRF for dataset preparation. Then create soft links by: ln -s /path/to/zju_mocap ./data/zju_mocap ln -s /path/to/deepcap ./data/deepcap ln -s /path/to/h36m ./data/h36m

  • Our synthetic dataset

    1. Download the dataset from Google Drive, this dataset is processed following NeuralBody.
    2. Create a soft link by: ln -s /path/to/mixamo ./data/mixamo

Train the model in 3 stages

1. Geometry and Motion Reconstruction

Training.

python train_geometry.py --cfg_file configs/geometry_ps_m3c.yaml exp_name geometry_ps_m3c

Visualize results of the first stage.

python run_geometry.py --type visualize --cfg_file configs/geometry_ps_m3c.yaml exp_name geometry_ps_m3c

2. Light Visibility Estimation

Generate training data.

python run_geometry.py --type visualize --cfg_file configs/geometry_ps_m3c.yaml exp_name geometry_ps_m3c gen_lvis_mesh True

Train light visibility model.

python train_lvis.py --cfg_file configs/geometry_ps_m3c.yaml exp_name lvis_ps_m3c exp_name_geo geometry_ps_m3c

3. Material and Lighting

Training.

python train_material.py --cfg_file configs/material_ps_m3c.yaml exp_name material_ps_m3c

Visualize results of the last stage, relighting with the reconstructed light.

python run_material.py --type visualize --cfg_file configs/material_ps_m3c.yaml exp_name material_ps_m3c

TODO

  • More datasets (Human3.6M, DeepCap and our synthetic dataset) and pretrained models.
  • Release the synthetic dataset.

Citation

If you find our work useful in your research, please consider citing:

@article{lin2023relightable,
    title={Relightable and Animatable Neural Avatars from Videos},
    author={Lin, Wenbin and Zheng, Chengwei and Yong, Jun-Hai and Xu, Feng},
    journal={arXiv preprint arXiv:2312.12877},
    year={2023}
}

Acknowledgements: This repository is built on top of the AnimableNeRF codebase, and part of our code is inherited from InvRender. We are grateful to the authors for releasing their code.