Video-Guided Foley Sound Generation with Multimodal Controls

• URL: http://arxiv.org/abs/2411.17698v1
• Date: Tue, 26 Nov 2024 18:59:58 GMT
• Title: Video-Guided Foley Sound Generation with Multimodal Controls
• Authors: Ziyang Chen, Prem Seetharaman, Bryan Russell, Oriol Nieto, David Bourgin, Andrew Owens, Justin Salamon,
• Abstract summary: MultiFoley is a model designed for video-guided sound generation. It supports multimodal conditioning through text, audio, and video. A key novelty of our model lies in its joint training on both internet video datasets with low-quality audio.
• Score: 30.515964061350395
• License:
• 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/

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