[](https://arxiv.org/abs/2410.07971)
[](https://xg-chu.site/project_gagavatar/)
[](https://www.youtube.com/watch?v=9244ZgOl4Xk)
[](https://github.com/xg-chu/GAGAvatar_track/)
[Xuangeng Chu](https://xg-chu.site)1 
[Tatsuya Harada](https://www.mi.t.u-tokyo.ac.jp/harada/)1,2
1The University of Tokyo,
2RIKEN AIP
🤩 NeurIPS 2024 🤩
GAGAvatar reconstructs controllable 3D head avatars from single images.
GAGAvatar achieves one-shot 3DGS-based head reconstruction and ⚡️real-time⚡️ reenactment.
🔥 More results can be found in our [Project Page](https://xg-chu.github.io/project_gagavatar/). 🔥
Installation
Clone the project
git clone --recurse-submodules git@github.com:xg-chu/GAGAvatar.git
cd GAGAvatar
Build environment
conda env create -f environment.yml
conda activate GAGAvatar
Install the 3DGS renderer
What’s the difference between this version and the original 3DGS?
- We changed the number of channels so that 3D Gaussians carry 32-dim features.
- We changed the package name to avoid conflict with the original Gaussian splatting.
git clone --recurse-submodules git@github.com:xg-chu/diff-gaussian-rasterization.git
pip install ./diff-gaussian-rasterization
rm -rf ./diff-gaussian-rasterization
Prepare resources
Prepare resources with:
bash ./build_resources.sh
When GAGAvatar is installed as a package, the asset manifest can be used instead:
gagavatar-assets download --root assets
This downloads only assets with explicit upstream sources and verifies known sizes and SHA-256 hashes. FLAME and tracker resources are represented in the manifest but are not downloaded automatically because they have separate setup or license requirements.
Also prepare resources for GAGAvatar_track using:
cd core/libs/GAGAvatar_track
bash ./build_resources.sh
Quick Start Guide
Driven by another image:
# This will track the images online, which is slow.
python inference.py -d ./demos/examples/2.jpg -i ./demos/examples/1.jpg
Driven by a tracked video:
python inference.py -d ./demos/drivers/obama -i ./demos/examples/1.jpg
Driven by a tracked image_lmdb:
python inference.py -d ./demos/drivers/vfhq_demo -i ./demos/examples/1.jpg
To test the inference speed, refer to the speed_test() function in inference.py.
To test your own images online, refer to lines 52-55 in inference.py.
To test your own driving sequences (videos/images), refer to GAGAvatar_track and demo sequences to build your own driving sequence.
Training Guide
You can use the pre-trained model directly, but if you need to retrain on your data:
Step 1: Building the image LMDB
Build img_lmdb yourself.
All the images should be cropped as inference. (Refer to line 218 in core/libs/GAGAvatar_track/engines/engine_core.py)
Dump images using core/libs/utils_lmdb.py, there is also an API for building lmdb: dump(key_name, payload), payload should be tensor with (3, 512, 512), in [0, 255].
015252 is video id (used when sampling), 99 is frame id (0 is the first frame, other frames id can be discontinuous).
img_lmdb:
'015252_99' : image payload
Step 2: Track the image LMDB
Using track_lmdb.py in GAGAvatar_track, you should get a optim.pkl.
optim.pkl:
- dict_keys(['000000_0', …])
- "000000_0": dict_keys(['bbox', 'shapecode', 'expcode', 'posecode', 'eyecode', 'transform_matrix'])
Step 3: Split the dataset
Build dataset.json yourself, it should contain the keys in img_lmdb and optim.pkl.
dataset.json: {
"train": ["000000_0", "000000_5", ..., '001384_654'],
"val": ["015209_0", ..., "015218_7"],
"test": ["015203_0", ..., "015252_139"]
}
Step 4: Modify the config and train
python train.py --config gaga --dataset vfhq
Citation
If you find our work useful in your research, please consider citing:
@inproceedings{
chu2024gagavatar,
title={Generalizable and Animatable Gaussian Head Avatar},
author={Xuangeng Chu and Tatsuya Harada},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=gVM2AZ5xA6}
}
Acknowledgements
Some part of our work is built based on FLAME, StyleMatte, EMICA and VGGHead. The GAGAvatar Logo is designed by Caihong Ning. We also thank the following projects for sharing their great work.
- GPAvatar: https://github.com/xg-chu/GPAvatar
- FLAME: https://flame.is.tue.mpg.de
- StyleMatte: https://github.com/chroneus/stylematte
- EMICA: https://github.com/radekd91/inferno
- VGGHead: https://github.com/KupynOrest/head_detector