[4/3/2024] The pretrained models for the other three people from People Snapshot are released on OneDrive.
[7/2/2024] The scripts for your own video are released.
[23/1/2024] Training and inference codes for People Snapshot are released.
We present GaussianAvatar, an efficient approach to creating realistic human avatars with dynamic 3D appearances from a single video.
To deploy and run GaussianAvatar, run the following scripts:
conda env create --file environment.yml
conda activate gs-avatar
Then, compile diff-gaussian-rasterization
and simple-knn
as in 3DGS repository.
SMPL/SMPL-X model: register and download SMPL and SMPL-X, and put these files in assets/smpl_files
. The folder should have the following structure:
smpl_files
└── smpl
├── SMPL_FEMALE.pkl
├── SMPL_MALE.pkl
└── SMPL_NEUTRAL.pkl
└── smplx
├── SMPLX_FEMALE.npz
├── SMPLX_MALE.npz
└── SMPLX_NEUTRAL.npz
Data: download the provided data from OneDrive. These data include assets.zip
, gs_data.zip
and pretrained_models.zip
. Please unzip assets.zip
to the corresponding folder in the repository and unzip others to gs_data_path
and pretrained_models_path
.
We take the subject m4c_processed
for example.
python train.py -s $gs_data_path/m4c_processed -m output/m4c_processed --train_stage 1
python eval.py -s $gs_data_path/m4c_processed -m output/m4c_processed --epoch 200
python render_novel_pose.py -s $gs_data_path/m4c_processed -m output/m4c_processed --epoch 200
scripts/custom/process-sequence.sh
in InstantAvatar. The data folder should have the followings:
smpl_files
├── images
├── masks
├── cameras.npz
└── poses_optimized.npz
path
in L50 and L51):
cd scripts & python sample_romp2gsavatar.py
path
)
python gen_pose_map_cano_smpl.py
cd .. & python train.py -s $path_to_data/$subject -m output/{$subject}_stage1 --train_stage 1 --pose_op_start_iter 10
cd scripts & python export_stage_1_smpl.py
python render_pred_smpl.py
python gen_pose_map_our_smpl.py
cd .. & python train.py -s $path_to_data/$subject -m output/{$subject}_stage2 --train_stage 2 --stage1_out_path $path_to_stage1_net_save_path
If you find this code useful for your research, please consider citing:
@inproceedings{hu2024gaussianavatar,
title={GaussianAvatar: Towards Realistic Human Avatar Modeling from a Single Video via Animatable 3D Gaussians},
author={Hu, Liangxiao and Zhang, Hongwen and Zhang, Yuxiang and Zhou, Boyao and Liu, Boning and Zhang, Shengping and Nie, Liqiang},
booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2024}
}
This project is built on source codes shared by Gaussian-Splatting, POP, HumanNeRF and InstantAvatar.