# DiffuseStyleGesture: Stylized Audio-Driven Co-Speech Gesture Generation with Diffusion Models

[![arXiv](https://img.shields.io/badge/arXiv-2305.04919-red.svg)](https://arxiv.org/abs/2305.04919)



<div align=center>
<img src="Framework.png" width="500px">
</div>


## News

📢 **9/May/23** - First release - arxiv, code and pre-trained models.


## 1. Getting started

This code was tested on `NVIDIA GeForce RTX 2080 Ti` and requires:

* conda3 or miniconda3

```
conda create -n DiffuseStyleGesture python=3.7
pip install -r requirements.txt 
```

[//]: # (-i https://pypi.tuna.tsinghua.edu.cn/simple)

## 2. Quick Start

1. Download pre-trained model from [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/f/8ade7c73e05c4549ac6b/) or [Google Cloud](https://drive.google.com/file/d/1RlusxWJFJMyauXdbfbI_XreJwVRnrBv_/view?usp=share_link)
and put it into `./main/mydiffusion_zeggs/`.
2. Download the [WavLM Large](https://github.com/microsoft/unilm/tree/master/wavlm) and put it into `./main/mydiffusion_zeggs/WavLM/`.
3. cd `./main/mydiffusion_zeggs/` and run 
```python
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](https://www.blender.org/).

## 3. Train your own model

### (1) Get ZEGGS dataset

Same as [ZEGGS](https://github.com/ubisoft/ubisoft-laforge-ZeroEGGS).

An example is as follows.
Download original ZEGGS datasets from [here](https://github.com/ubisoft/ubisoft-laforge-ZeroEGGS) 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](https://cloud.tsinghua.edu.cn/f/ba5f3b33d94b4cba875b/) or [Baidu Cloud](https://pan.baidu.com/s/1KakkGpRZWfaJzfN5gQvPAw?pwd=vfuc).
And put it in `./ubisoft-laforge-ZeroEGGS-main/data/processed_v1/trimmed/` folder.


### (2) Process ZEGGS dataset

```
cd ./main/mydiffusion_zeggs/
python zeggs_data_to_lmdb.py
```

### (3) Train

```
python end2end.py --config=./configs/DiffuseStyleGesture.yml --no_cuda 0 --gpu 0
```
The model will save in `./main/mydiffusion_zeggs/zeggs_mymodel3_wavlm/` folder.

## Reference
Our work mainly inspired by: [MDM](https://github.com/GuyTevet/motion-diffusion-model), [Text2Gesture](https://github.com/youngwoo-yoon/Co-Speech_Gesture_Generation), [Listen, denoise, action!](https://arxiv.org/abs/2211.09707)

## Citation
If you find this code useful in your research, please cite:

```
@inproceedings{yang2023DiffuseStyleGesture,
  author       = {Sicheng Yang and Zhiyong Wu and Minglei Li and Zhensong Zhang and Lei Hao and Weihong Bao and Ming Cheng and Long Xiao},
  title        = {DiffuseStyleGesture: Stylized Audio-Driven Co-Speech Gesture Generation with Diffusion Models},
  booktitle    = {Proceedings of the 32nd International Joint Conference on Artificial Intelligence, {IJCAI} 2023},
  publisher    = {ijcai.org},
  year         = {2023},
}
```

Please feel free to contact us ([yangsc21@mails.tsinghua.edu.cn](yangsc21@mails.tsinghua.edu.cn)) with any question or concerns.