Video-Guided Foley Sound Generation with Multimodal Controls
Authors: name: Ziyang Chen, name: Prem Seetharaman, name: Bryan Russell, name: Oriol Nieto, name: David Bourgin, name: Andrew Owens, name: Justin Salamon

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/