@inproceedings{
xu2024vasa,
title={{VASA}-1: Lifelike Audio-Driven Talking Faces Generated in Real Time},
author={Sicheng Xu and Guojun Chen and Yu-Xiao Guo and Jiaolong Yang and Chong Li and Zhenyu Zang and Yizhong Zhang and Xin Tong and Baining Guo},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=5zSCSE0k41}
}
We introduce VASA, a framework for generating lifelike talking faces with appealing visual affective skills (VAS) given a single static image and a speech audio clip. Our premiere model, VASA-1, is capable of not only generating lip movements that are exquisitely synchronized with the audio, but also producing a large spectrum of facial nuances and natural head motions that contribute to the perception of authenticity and liveliness. 
The core innovations include a diffusion-based holistic facial dynamics and head movement generation model that works in a face latent space, and the development of such an expressive and disentangled face latent space using videos.
Through extensive experiments including evaluation on a set of new metrics, we show that our method significantly outperforms previous methods along various dimensions comprehensively. Our method delivers high video quality with realistic facial and head dynamics and also supports the online generation of 512$\times$512 videos at up to 40 FPS with negligible starting latency.
It paves the way for real-time engagements with lifelike avatars that emulate human conversational behaviors.
VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Time
Sicheng Xu Guojun Chen Yu-Xiao Guo Jiaolong Yang Chong Li Zhenyu Zang Yizhong Zhang Xin Tong Baining Guo