AC-Foley: Reference-Audio-Guided Video-to-Audio Synthesis with Acoustic Transfer
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914410906255360 |
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| author | Fang, Pengjun He, Yingqing Xing, Yazhou Chen, Qifeng Lim, Ser-Nam Yang, Harry |
| author_facet | Fang, Pengjun He, Yingqing Xing, Yazhou Chen, Qifeng Lim, Ser-Nam Yang, Harry |
| contents | Existing video-to-audio (V2A) generation methods predominantly rely on text prompts alongside visual information to synthesize audio. However, two critical bottlenecks persist: semantic granularity gaps in training data, such as conflating acoustically distinct sounds under coarse labels, and textual ambiguity in describing micro-acoustic features. These bottlenecks make it difficult to perform fine-grained sound synthesis using text-controlled modes. To address these limitations, we propose AC-Foley, an audio-conditioned V2A model that directly leverages reference audio to achieve precise and fine-grained control over generated sounds. This approach enables fine-grained sound synthesis, timbre transfer, zero-shot sound generation, and improved audio quality. By directly conditioning on audio signals, our approach bypasses the semantic ambiguities of text descriptions while enabling precise manipulation of acoustic attributes. Empirically, AC-Foley achieves state-of-the-art performance for Foley generation when conditioned on reference audio, while remaining competitive with state-of-the-art video-to-audio methods even without audio conditioning. Code and demo are available at: https://ff2416.github.io/AC-Foley-Page |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_15597 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AC-Foley: Reference-Audio-Guided Video-to-Audio Synthesis with Acoustic Transfer Fang, Pengjun He, Yingqing Xing, Yazhou Chen, Qifeng Lim, Ser-Nam Yang, Harry Sound Computer Vision and Pattern Recognition Machine Learning Multimedia Audio and Speech Processing Existing video-to-audio (V2A) generation methods predominantly rely on text prompts alongside visual information to synthesize audio. However, two critical bottlenecks persist: semantic granularity gaps in training data, such as conflating acoustically distinct sounds under coarse labels, and textual ambiguity in describing micro-acoustic features. These bottlenecks make it difficult to perform fine-grained sound synthesis using text-controlled modes. To address these limitations, we propose AC-Foley, an audio-conditioned V2A model that directly leverages reference audio to achieve precise and fine-grained control over generated sounds. This approach enables fine-grained sound synthesis, timbre transfer, zero-shot sound generation, and improved audio quality. By directly conditioning on audio signals, our approach bypasses the semantic ambiguities of text descriptions while enabling precise manipulation of acoustic attributes. Empirically, AC-Foley achieves state-of-the-art performance for Foley generation when conditioned on reference audio, while remaining competitive with state-of-the-art video-to-audio methods even without audio conditioning. Code and demo are available at: https://ff2416.github.io/AC-Foley-Page |
| title | AC-Foley: Reference-Audio-Guided Video-to-Audio Synthesis with Acoustic Transfer |
| topic | Sound Computer Vision and Pattern Recognition Machine Learning Multimedia Audio and Speech Processing |
| url | https://arxiv.org/abs/2603.15597 |