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arXiv · 2608.15522

Efficient Audio-Visual Generation via Synchrony-Aware Cross-Modal Sparse Attention

Shengchuan Gao·Teng Hu·Bohao Feng·Luchen Li·Wenqiang Wang·Hongqian Deng·Ran Yi
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Abstract

Recent audio-visual generation models can synthesize synchronized video and sound in a unified diffusion process, but their inference cost remains high because long video token sequences require repeated attention computation across denoising steps. A variety of acceleration techniques have been developed for video generation models, including low-bit quantization, attention sparsification, and feature caching. However, since these methods are originally designed for video generation, directly applying them to audio-visual models overlooks the interactions between the audio and video branches and may therefore disrupt audio-video synchronization. We present a synchronization-aware acceleration framework for efficient audio-visual generation. Our key observation is that bidirectional audio-video cross-attention reveals structured interactions between the two branches, with high responses often concentrated on a few sound-related visual and temporal regions. Guided by this interaction pattern, we introduce a protected sparse attention strategy that preserves high-fidelity computation for synchronization-critical tokens while sparsifying redundant attention interactions. By explicitly accounting for cross-modal dependence during acceleration, our method improves inference efficiency while keeping video quality, audio quality, and audio-video synchronization.

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Shengchuan Gao, Teng Hu, Bohao Feng, Luchen Li, Wenqiang Wang, Hongqian Deng, Ran Yi. 2026-08-16. Efficient Audio-Visual Generation via Synchrony-Aware Cross-Modal Sparse Attention. https://arxiv.org/abs/2608.15522

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