Learning Visual Styles from Audio-Visual Associations

• URL: http://arxiv.org/abs/2205.05072v1
• Date: Tue, 10 May 2022 17:57:07 GMT
• Title: Learning Visual Styles from Audio-Visual Associations
• Authors: Tingle Li, Yichen Liu, Andrew Owens, Hang Zhao
• Abstract summary: We present a method for learning visual styles from unlabeled audio-visual data. Our model learns to manipulate the texture of a scene to match a sound. We show that audio can be an intuitive representation for manipulating images.
• Score: 21.022027778790978
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: From the patter of rain to the crunch of snow, the sounds we hear often convey the visual textures that appear within a scene. In this paper, we present a method for learning visual styles from unlabeled audio-visual data. Our model learns to manipulate the texture of a scene to match a sound, a problem we term audio-driven image stylization. Given a dataset of paired audio-visual data, we learn to modify input images such that, after manipulation, they are more likely to co-occur with a given input sound. In quantitative and qualitative evaluations, our sound-based model outperforms label-based approaches. We also show that audio can be an intuitive representation for manipulating images, as adjusting a sound's volume or mixing two sounds together results in predictable changes to visual style. Project webpage: https://tinglok.netlify.app/files/avstyle

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