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arXiv · 2306.17203

Diff-Foley: Synchronized Video-to-Audio Synthesis with Latent Diffusion Models

Simian Luo·Chuanhao Yan·Chenxu Hu·Hang Zhao
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Abstract

The Video-to-Audio (V2A) model has recently gained attention for its practical application in generating audio directly from silent videos, particularly in video/film production. However, previous methods in V2A have limited generation quality in terms of temporal synchronization and audio-visual relevance. We present Diff-Foley, a synchronized Video-to-Audio synthesis method with a latent diffusion model (LDM) that generates high-quality audio with improved synchronization and audio-visual relevance. We adopt contrastive audio-visual pretraining (CAVP) to learn more temporally and semantically aligned features, then train an LDM with CAVP-aligned visual features on spectrogram latent space. The CAVP-aligned features enable LDM to capture the subtler audio-visual correlation via a cross-attention module. We further significantly improve sample quality with `double guidance'. Diff-Foley achieves state-of-the-art V2A performance on current large scale V2A dataset. Furthermore, we demonstrate Diff-Foley practical applicability and generalization capabilities via downstream finetuning. Project Page: see https://diff-foley.github.io/

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Simian Luo, Chuanhao Yan, Chenxu Hu, Hang Zhao. 2023-06-29. Diff-Foley: Synchronized Video-to-Audio Synthesis with Latent Diffusion Models. https://arxiv.org/abs/2306.17203

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