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<div align="center"><b>Monogaussianavatar</b>: Monocular gaussian point-based head avatar
Yufan Chen<sup>†,1</sup>, Lizhen Wang<sup>2</sup>, Qijing Li<sup>2</sup>, Hongjiang Xiao<sup>3</sup>, Shengping Zhang<sup>*,1</sup>, Hongxun Yao<sup>1</sup>, Yebin Liu<sup>2</sup>
<p><sup>1</sup>Harbin Institute of Technology <sup>2</sup>Tsinghua Univserity <sup>3</sup>Communication University of China <br><sup>*</sup>Corresponding author <sup>†</sup>Work done during an internship at Tsinghua Univserity<p>Paper | Video Youtube | Project Page
<img src="assets/teaser.png" width="800" height="350"/> </div>Getting Started
- Create a conda or python environment and activate. For e.g.,
conda create -n monogshead python=3.9; conda activate monogshead. - Install PyTorch 1.11.0 with
conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch. This version works with both PyTorch3d and functorch. - Install PyTorch3d:
conda install -c fvcore -c iopath -c conda-forge fvcore iopath
conda install pytorch3d
- Install other requirements:
cd ../monogaussianavatar; pip install -r requirement.txt; pip install -U face-detection-tflite - Install gaussian rasterization:
cd submodules/
git clone https://github.com/graphdeco-inria/gaussian-splatting --recursive
cd gaussian-splatting/
pip install -e submodules/diff-gaussian-rasterization
cd ..
- Download FLAME model, choose FLAME 2020 and unzip it, copy 'generic_model.pkl' into
./code/flame/FLAME2020
Preparing dataset
Our data format is the same as IMavatar. You can download a preprocessed dataset from Google drive (subject 1 and 2)
If you'd like to generate your own dataset, please follow intructions in the IMavatar repo.
Link the dataset folder to ./data/datasets. Link the experiment output folder to ./data/experiments.
Pre-trained model
Download a pretrained model from .
Uncompress and put into the experiment folder ./data/experiments.
Training
python scripts/exp_runner.py ---conf ./confs/subject1.conf [--is_continue]
Evaluation
Set the is_eval flag for evaluation, optionally set checkpoint (if not, the latest checkpoint will be used) and load_path
python scripts/exp_runner.py --conf ./confs/subject1.conf --is_eval [--checkpoint 60] [--load_path ...]
GPU requirement
We train our models with a single Nvidia 24GB RTX3090 GPU.
Citation
If you find our code or paper useful, please cite as:
@inproceedings{chen2024monogaussianavatar,
title={Monogaussianavatar: Monocular gaussian point-based head avatar},
author={Chen, Yufan and Wang, Lizhen and Li, Qijing and Xiao, Hongjiang and Zhang, Shengping and Yao, Hongxun and Liu, Yebin},
booktitle={ACM SIGGRAPH 2024 Conference Papers},
pages={1--9},
year={2024}
}