This project is a pytorch implementation of SEEG: Semantic Energized Co-speech Gesture Generation.
- Only learning beat gestures already performs comparably with the SOTA methods.
- Introducing additional semantic-aware supervision can influence the gestures expressions.
This repository is developed and tested on Ubuntu 18.04, Python 3.6+, and PyTorch 1.3+. The environment is the same to Trimodal Context.
This project is mainly developed based on Trimodal Context. You can run this project by bash train.sh or the same commands in Trimodal Context.
Please cite our CVPR2022 paper if you find our SEEG is helpful in your work:
@inproceedings{liang2022seeg,
title={SEEG: Semantic Energized Co-speech Gesture Generation},
author={Liang, Yuanzhi and Feng, Qianyu and Zhu, Linchao and Hu, Li and Pan, Pan and Yang, Yi},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2022}
}