JoyAvatar: Real-time and Infinite Audio-Driven Avatar Generation with Autoregressive Diffusion

• URL: http://arxiv.org/abs/2512.11423v1
• Date: Fri, 12 Dec 2025 10:06:01 GMT
• Title: JoyAvatar: Real-time and Infinite Audio-Driven Avatar Generation with Autoregressive Diffusion
• Authors: Chaochao Li, Ruikui Wang, Liangbo Zhou, Jinheng Feng, Huaishao Luo, Huan Zhang, Youzheng Wu, Xiaodong He,
• Abstract summary: JoyAvatar is an audio-driven autoregressive model capable of real-time inference and infinite-length video generation.<n>Our model achieves competitive results in visual quality, temporal consistency, and lip synchronization.
• Score: 19.420963062956222
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing DiT-based audio-driven avatar generation methods have achieved considerable progress, yet their broader application is constrained by limitations such as high computational overhead and the inability to synthesize long-duration videos. Autoregressive methods address this problem by applying block-wise autoregressive diffusion methods. However, these methods suffer from the problem of error accumulation and quality degradation. To address this, we propose JoyAvatar, an audio-driven autoregressive model capable of real-time inference and infinite-length video generation with the following contributions: (1) Progressive Step Bootstrapping (PSB), which allocates more denoising steps to initial frames to stabilize generation and reduce error accumulation; (2) Motion Condition Injection (MCI), enhancing temporal coherence by injecting noise-corrupted previous frames as motion condition; and (3) Unbounded RoPE via Cache-Resetting (URCR), enabling infinite-length generation through dynamic positional encoding. Our 1.3B-parameter causal model achieves 16 FPS on a single GPU and achieves competitive results in visual quality, temporal consistency, and lip synchronization.

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