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
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 producing
lip movements that are exquisitely synchronized with the audio, but also
capturing a large spectrum of facial nuances and natural head motions that
contribute to the perception of authenticity and liveliness. The core
innovations include a 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 not only delivers high video quality with realistic
facial and head dynamics but also supports the online generation of 512x512
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.