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Generating high quality music that complements the visual content of a videois a challenging task. Most existing visual conditioned music generationsystems generate symbolic music data, such as MIDI files, instead of raw audiowaveform. Given the limited availability of symbolic music data, such methodscan only generate music for a few instruments or for specific types of visualinput. In this paper, we propose a novel approach called V2Meow that cangenerate high-quality music audio that aligns well with the visual semantics ofa diverse range of video input types. Specifically, the proposed musicgeneration system is a multi-stage autoregressive model which is trained with anumber of O(100K) music audio clips paired with video frames, which are minedfrom in-the-wild music videos, and no parallel symbolic music data is involved.V2Meow is able to synthesize high-fidelity music audio waveform solelyconditioned on pre-trained visual features extracted from an arbitrary silentvideo clip, and it also allows high-level control over the music style ofgeneration examples via supporting text prompts in addition to the video framesconditioning. Through both qualitative and quantitative evaluations, wedemonstrate that our model outperforms several existing music generationsystems in terms of both visual-audio correspondence and audio quality.

V2Meow: Meowing to the Visual Beat via Music Generation