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

Simian Luo, Chuanhao Yan, Chenxu Hu, Hang Zhao

Keywords: Video-to-Audio Generation; Contrastive Audio-Visual Pretraining; Latent Diffusion Model;

TL;DR: Diff-Foley is a synchronized Video-to-Audio synthesis method with a latent diffusion model that generates high quality, highly synchronized audio with improved audio-visual relevance. It outperforms previous methods by a large margin.

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 adaptability via customized downstream finetuning. Project Page: https://diff-foley.github.io/

Submission Number: 2021