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PointAvatar: Deformable Point-based Head Avatars from Videos

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Official Repository for CVPR 2023 paper PointAvatar: Deformable Point-based Head Avatars from Videos. <img src="assets/pointavatar_teaser_v6.png" width="800" height="223"/>

Getting Started

• Clone this repo: git clone git@github.com:zhengyuf/pointavatar.git
• Create a conda or python environment and activate. For e.g., conda create -n point-avatar python=3.9; conda activate point-avatar.
• Install PyTorch 1.11.0 with conda or pip (instructions). This version works with both PyTorch3d and functorch.
• We made small modifications to PyTorch3d (0.6.2), so clone our version and install:

git clone git@github.com:zhengyuf/pytorch3d.git
cd pytorch3d
git checkout point-avatar
pip install -e .

• Install other requirements: cd ../pointavatar; pip install -r requirement.txt
• 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 the ETH Zurich server (subject 1, subject 2 and subject 3). You can run download_data.bash to download both datasets and pre-trained models.

If you'd like to generate your own dataset, please follow the instructions 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 ETH Zurich server (subject 1, subject 2 and subject 3). See download_data.bash. 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 80GB A100 GPU. It's also possible to train PointAvatar with less CUDA memory by limiting the number of points. For e.g., with a 24GB Quadro RTX 6000, you can set train.max_points_training and point_cloud.max_points to 240000. For evaluation, by default, the latest checkpoint is used. To reduce memory usage, you can use earlier checkpoints by adding —-checkpoint 60 (or 55).

Citation

If you find our code or paper useful, please cite as:

@inproceedings{Zheng2023pointavatar,
  author    = {Yufeng Zheng and Wang Yifan and Gordon Wetzstein and Michael J. Black and Otmar Hilliges},
  title     = {PointAvatar: Deformable Point-based Head Avatars from Videos},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  year = {2023}
}