AC-Foley: Reference-Audio-Guided Video-to-Audio Synthesis with Acoustic Transfer
Pengjun Fang, Yingqing He, Yazhou Xing, Qifeng Chen, Ser-Nam Lim, Harry Yang
摘要
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 (e.g., conflating acoustically distinct sounds like different dog barks under coarse labels), and textual ambiguity in describing microacoustic features (e.g., "metallic clang" failing to distinguish impact transients and resonance decay). 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 (e.g., footsteps with distinct timbres on wood, marble, or gravel), timbre transfer (e.g., transforming a violin’s melody into the bright, piercing tone of a suona), zero-shot generation of sounds (e.g., creating unique weapon sound effects without training on firearm datasets) and better 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 SOTA video-to-audio methods even without audio conditioning.
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它引用的顶会 Paper13
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- Diff-Foley: Synchronized Video-to-Audio Synthesis with Latent Diffusion ModelsSimian Luo, Chuanhao Yan, Chenxu Hu, Hang ZhaoNeurIPS 2023 · 被引用 192 次
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- Read, Watch and Scream! Sound Generation from Text and VideoYujin Jeong, Yunji Kim, Sanghyuk Chun, Jiyoung LeeAAAI 2025 · 被引用 48 次
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