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https://github.com/JingLi513/Audio2Gestures
Audio2Motion Official implementation for Audio2Motion: Generating Diverse Gestures from Speech with Conditional Variational Autoencoders.
https://github.com/JingLi513/Audio2Gestures
Last synced: 3 months ago
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Audio2Motion Official implementation for Audio2Motion: Generating Diverse Gestures from Speech with Conditional Variational Autoencoders.
- Host: GitHub
- URL: https://github.com/JingLi513/Audio2Gestures
- Owner: JingLi513
- Created: 2021-08-06T14:56:09.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-01-01T23:52:33.000Z (10 months ago)
- Last Synced: 2024-04-23T20:34:40.551Z (7 months ago)
- Language: Python
- Size: 21.3 MB
- Stars: 116
- Watchers: 2
- Forks: 9
- Open Issues: 14
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Metadata Files:
- Readme: README.md
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README
# Audio2Gestures
Official implementation for Audio2Gestures: Generating Diverse Gestures from Speech Audio with Conditional Variational Autoencoders, ICCV## Dependencies
- [pytorch](https://pytorch.org/)
- [numpy](https://numpy.org/)
- [librosa](https://librosa.org/)
- [fbx](https://www.autodesk.com/developer-network/platform-technologies/fbx-sdk-2020-0)
- [smplx 1.0](https://github.com/vchoutas/smplx)## Data Processing
1. Download fbx format data from [Trinity speech gesture](https://trinityspeechgesture.scss.tcd.ie/).
2. Retarget the data into SMPLX model using maya.
3. convert the fbx data to hdf5 format using script ``fbx2hdf.py``## Training and testing
Run script ``bash start.sh``## Visualizing
Run script ``python .\smplx2fbx.py --smplx .\for_smplx_retargeting.h5 --key LclRotation --fps 30 --synthesized .\input.h5 --fbx output.fbx``## Reference
If you find our code useful for your research, please cite our paper.
> @inproceedings{li2021audio2gestures,
> title={Audio2Gestures: Generating Diverse Gestures from Speech Audio with Conditional Variational Autoencoders},
> author={Li, Jing and Kang, Di and Pei, Wenjie and Zhe, Xuefei and Zhang, Ying and He, Zhenyu and Bao, Linchao},
> booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
> pages={11293--11302},
> year={2021}
> }