  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/
