Video-Guided Foley Sound Generation with Multimodal Controls
Abstract
Generating sound effects for videos often requires creating artistic sound effects that diverge significantly from real-life sources and flexible control in the sound design. To address this problem, we introduce MultiFoley, a model designed for video-guided sound generation that supports multimodal conditioning through text, audio, and video. Given a silent video and a text prompt, MultiFoley allows users to create clean sounds (e.g., skateboard wheels spinning without wind noise) or more whimsical sounds (e.g., making a lion's roar sound like a cat's meow). MultiFoley also allows users to choose reference audio from sound effects (SFX) libraries or partial videos for conditioning. A key novelty of our model lies in its joint training on both internet video datasets with low-quality audio and professional SFX recordings, enabling high-quality, full-bandwidth (48kHz) audio generation. Through automated evaluations and human studies, we demonstrate that MultiFoley successfully generates synchronized high-quality sounds across varied conditional inputs and outperforms existing methods. Please see our project page for video results: https://ificl.github.io/MultiFoley/
Cite
@article{arxiv.2411.17698,
title = {Video-Guided Foley Sound Generation with Multimodal Controls},
author = {Ziyang Chen and Prem Seetharaman and Bryan Russell and Oriol Nieto and David Bourgin and Andrew Owens and Justin Salamon},
journal= {arXiv preprint arXiv:2411.17698},
year = {2025}
}
Comments
Accepted at CVPR 2025. Project site: https://ificl.github.io/MultiFoley/