DiffSHEG: A Diffusion-Based Approach for Real-Time Speech-driven Holistic 3D Expression and Gesture Generation

Junming Chen, Yunfei Liu, Jianan Wang, Ailing Zeng, Yu Li, Qifeng Chen; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 7352-7361

We propose DiffSHEG a Diffusion-based approach for Speech-driven Holistic 3D Expression and Gesture generation. While previous works focused on co-speech gesture or expression generation individually the joint generation of synchronized expressions and gestures remains barely explored. To address this our diffusion-based co-speech motion generation Transformer enables uni-directional information flow from expression to gesture facilitating improved matching of joint expression-gesture distributions. Furthermore we introduce an outpainting-based sampling strategy for arbitrary long sequence generation in diffusion models offering flexibility and computational efficiency. Our method provides a practical solution that produces high-quality synchronized expression and gesture generation driven by speech. Evaluated on two public datasets our approach achieves state-of-the-art performance both quantitatively and qualitatively. Additionally a user study confirms the superiority of our method over prior approaches. By enabling the real-time generation of expressive and synchronized motions our method showcases its potential for various applications in the development of digital humans and embodied agents.