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arXiv · 2606.03672

Foley-Omni: A Unified Multimodal Generation Model from Task-Level Audio Synthesis to Complete Video Soundtrack Generation

Ye Tao·Lupeng Liu·Xuenan Xu·Jiasun Feng·Jiarui Wang·Ying Qin·Shuiyang Mao·Wei Liu·Shuai Wang
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Abstract

Recent unified audio generation models can support diverse tasks across speech, sound effects, and music, but most of them still focus on isolated task-level synthesis. However, real video production often requires multiple components of a complete audio track to be generated jointly and consistently for the same video. We present Foley-Omni, a unified multimodal audio generation model that extends isolated task-level synthesis to complete video soundtrack generation by jointly modeling speech, sound effects, and music within a shared latent generation process. To support training and reproducible evaluation, we develop an audiovisual data curation pipeline and introduce V2ST-Bench, a benchmark for holistic video soundtrack generation evaluation. Experiments show that Foley-Omni achieves competitive performance with expert systems on individual synthesis tasks, while improving speech intelligibility, audiovisual consistency and perceptual quality for mixed soundtrack generation.

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Ye Tao, Lupeng Liu, Xuenan Xu, Jiasun Feng, Jiarui Wang, Ying Qin, Shuiyang Mao, Wei Liu, Shuai Wang. 2026-06-02. Foley-Omni: A Unified Multimodal Generation Model from Task-Level Audio Synthesis to Complete Video Soundtrack Generation. https://arxiv.org/abs/2606.03672

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