📢 QPGesture - Based on motion matching, the upper body gesture.
📢 UnifiedGesture - Training on multiple gesture datasets, refine the gestures.
📢 9/Oct/23 - We obtained the REPRODUCIBILITY AWARD by GENEA Committee, so we strongly recommend trying DiffuseStyleGesture+ in advance compared to code of DiffuseStyleGesture is partially optimized.
📢 29/Aug/23 - Release the paper of DiffuseStyleGesture+, refer to the official paper of GENEA Challenge 2023 to get more.
📢 5/Aug/23 - Release code and pre-trained models of DiffuseStyleGesture+ on BEAT and TWH.
📢 31/Jul/23 - Upload a tutorial video on visualizing gestures.
📢 25/Jun/23 - Upload presentation video.
📢 9/May/23 - First release - arxiv, demo, code, pre-trained models on ZEGGS and issue.
This code was tested on NVIDIA GeForce RTX 2080 Ti
and requires:
- conda3 or miniconda3
conda create -n DiffuseStyleGesture python=3.7
conda activate DiffuseStyleGesture
pip install -r requirements.txt
- Download pre-trained model from Tsinghua Cloud or Google Cloud
and put it into
./main/mydiffusion_zeggs/
. - Download the WavLM Large and put it into
./main/mydiffusion_zeggs/WavLM/
. - cd
./main/mydiffusion_zeggs/
and run
python sample.py --config=./configs/DiffuseStyleGesture.yml --no_cuda 0 --gpu 0 --model_path './model000450000.pt' --audiowavlm_path "./015_Happy_4_x_1_0.wav" --max_len 320
You will get the .bvh
file named yyyymmdd_hhmmss_smoothing_SG_minibatch_320_[1, 0, 0, 0, 0, 0]_123456.bvh
in the sample_dir
folder, which can then be visualized using Blender with the following result (To visualize bvh with Blender see this issue and this tutorial video):
0001-0933.mp4
The parameter no_cuda
and gpu
need to be the same, i.e. the GPU you want to use; max_len
is the length you want to generate, this parameter should be 0
if you want to generate the whole length; if you want to use your own audio, you should rename your audio file name as xxx_style_xxx.wav
, e.g. 000_Neutral_xxx.wav
(Happy, Sad, ...). please refer to this issue to set the style and intensity you want.
Same as ZEGGS.
An example is as follows.
Download original ZEGGS datasets from here and put it in ./ubisoft-laforge-ZeroEGGS-main/data/
folder.
Then cd ./ubisoft-laforge-ZeroEGGS-main/ZEGGS
and run python data_pipeline.py
to process the dataset.
You will get ./ubisoft-laforge-ZeroEGGS-main/data/processed_v1/trimmed/train/
and ./ubisoft-laforge-ZeroEGGS-main/data/processed_v1/trimmed/test/
folders.
If you find it difficult to obtain and process the data, you can download the data after it has been processed by ZEGGS from Tsinghua Cloud or Baidu Cloud.
And put it in ./ubisoft-laforge-ZeroEGGS-main/data/processed_v1/trimmed/
folder.
cd ./main/mydiffusion_zeggs/
python zeggs_data_to_lmdb.py
python end2end.py --config=./configs/DiffuseStyleGesture.yml --no_cuda 0 --gpu 0
The model will save in ./main/mydiffusion_zeggs/zeggs_mymodel3_wavlm/
folder.
Our work mainly inspired by: MDM, Text2Gesture, Listen, denoise, action!
If you find this code useful in your research, please cite:
@inproceedings{ijcai2023p650,
title = {DiffuseStyleGesture: Stylized Audio-Driven Co-Speech Gesture Generation with Diffusion Models},
author = {Yang, Sicheng and Wu, Zhiyong and Li, Minglei and Zhang, Zhensong and Hao, Lei and Bao, Weihong and Cheng, Ming and Xiao, Long},
booktitle = {Proceedings of the Thirty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-23}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
pages = {5860--5868},
year = {2023},
month = {8},
doi = {10.24963/ijcai.2023/650},
url = {https://doi.org/10.24963/ijcai.2023/650},
}
Please feel free to contact us ([email protected]) with any question or concerns.