AvatarSync: Rethinking Talking-Head Animation through Phoneme-Guided Autoregressive Perspective

Abstract

Talking-head animation focuses on generating realistic facial videos from audio input. Following Generative Adversarial Networks (GANs), diffusion models have become the mainstream, owing to their robust generative capacities. However, inherent limitations of the diffusion process often lead to inter-frame flicker and slow inference, restricting their practical deployment. To address this, we introduce AvatarSync, an autoregressive framework on phoneme representations that generates realistic and controllable talking-head animations from a single reference image, driven directly by text or audio input. To mitigate flicker and ensure continuity, AvatarSync leverages an autoregressive pipeline that enhances temporal modeling. In addition, to ensure controllability, we introduce phonemes, which are the basic units of speech sounds, and construct a many-to-one mapping from text/audio to phonemes, enabling precise phoneme-to-visual alignment. Additionally, to further accelerate inference, we adopt a two-stage generation strategy that decouples semantic modeling from visual dynamics, and incorporate a customized Phoneme-Frame Causal Attention Mask to support multi-step parallel acceleration. Extensive experiments conducted on both Chinese (CMLR) and English (HDTF) datasets demonstrate that AvatarSync outperforms existing talking-head animation methods in visual fidelity, temporal consistency, and computational efficiency, providing a scalable and controllable solution.