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

• URL: http://arxiv.org/abs/2306.17203v1
• Date: Thu, 29 Jun 2023 12:39:58 GMT
• Title: Diff-Foley: Synchronized Video-to-Audio Synthesis with Latent Diffusion Models
• Authors: Simian Luo, Chuanhao Yan, Chenxu Hu, Hang Zhao
• Abstract summary: We present Diff-Foley, a synchronized Video-to-Audio synthesis method with a latent diffusion model (LDM) We show Diff-Foley achieves state-of-the-art V2A performance on current large scale V2A dataset.
• Score: 12.898486592791604
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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