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conda create -n Avatar python==3.6.8
conda install pytorch==1.7.0 torchvision==0.8.0 cudatoolkit=11.0 -c pytorch
or conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.2 -c pytorch
pip install -r requirements.txt
wget https://github.com/facebookresearch/pytorch3d/archive/refs/tags/v0.4.0.zip
cd pytorch3d
pip install -e .
cd thirdparty/neural_renderer_pytorch
python setup.py install
Please make sure your gcc version > 7.5 !
Download the assets files from here, unzip it and move them to the assets folder.
Download the pre-trained model from here, unzip it and move them to the checkpoints folder.
You first need to run Openpose, PifuHD and MODNet to generate 2d joints, normal and mask to train our model. Then the generated data should be organized as follows:
--data_dir
----frames_mat
------subject_name
----2d_joints
------subject_name
--------json
----mask_mat
------subject_name
----normal
------subject_name
We provide the sample data in this link.
First, to generate initial geometry by running:
python dynamic_offsets_runner.py --root_dir $data_dir --name $subject_name --device_id $device_id
Then, to generate texture map by running:
python texture_generation.py --root_dir $data_dir --name $subject_name --device_id $device_id
Copyright 2022 the 3D Vision Group at the College of Intelligence and Computing, Tianjin University. All Rights Reserved.
If you use this code in you work, please cite our publications.
Permission to use, copy, modify and distribute this software and its documentation for educational, research and non-profit purposes only. Any modification based on this work must be open source and prohibited for commercial use. You must retain, in the source form of any derivative works that you distribute, all copyright, patent, trademark, and attribution notices from the source form of this work.
If you find our work useful in your research, please consider citing:
@inproceedings{zhao2022avatar,
author = {Hao Zhao and Jinsong Zhang and Yu-Kun Lai and Zerong Zheng and Yingdi Xie and Yebin Liu and Kun Li},
title = {High-Fidelity Human Avatars from a Single RGB Camera},
booktitle = {CVPR},
year={2022},
}
We borrow some code from NeuralTexture, LWG. Thanks for their great contribtuions.