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<!DOCTYPE html>
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<title>GVMGen: A General Video-to-Music Generation Model With Hierarchical Attentions</title>
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<h1 class="title is-1 publication-title">GVMGen: A General Video-to-Music Generation Model With Hierarchical Attentions</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a href="https://scholar.google.com/citations?user=FOQkw5QAAAAJ" target="_blank">Heda Zuo</a>,</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=3nAQjbQAAAAJ" target="_blank">Weitao You</a><sup>*</sup>,</span>
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<a href="https://github.com/kita-wjx" target="_blank">Junxian Wu</a>,
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<a href="https://orcid.org/0000-0002-8415-7384" target="_blank">Shihong Ren</a>,
</span>
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<a href="https://scholar.google.com/citations?user=T4oYmPUAAAAJ" target="_blank">Pei Chen</a>,
</span>
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<a href="https://orcid.org/0009-0002-8479-2844" target="_blank">Mingxu Zhou</a>,
</span>
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<a href="https://scholar.google.com/citations?&user=HYia6aUAAAAJ" target="_blank">Yujia Lu</a>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=zzW8d-wAAAAJ" target="_blank">Lingyun Sun</a>,
</span>
</div>
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<span class="author-block">Zhejiang University, Shanghai Conservatory of Music<br>AAAI 2025</span>
<span class="eql-cntrb"><small><br><sup>*</sup>Corresponding Author</small></span>
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<h2 class="title is-3">Abstract</h2>
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<p>
Composing music for video is essential yet challenging, leading to a growing interest in automating music generation for video applications. Existing approaches often struggle to achieve robust music-video correspondence and generative diversity, primarily due to inadequate feature alignment methods and insufficient datasets. In this study, we present General Video-to-Music Generation model (GVMGen), designed for generating high-related music to the video input. Our model employs hierarchical attentions to extract and align video features with music in both spatial and temporal dimensions, ensuring the preservation of pertinent features while minimizing redundancy. Remarkably, our method is versatile, capable of generating multi-style music from different video inputs, even in zero-shot scenarios. We also propose an evaluation model along with two novel objective metrics for assessing video-music alignment. Additionally, we have compiled a large-scale dataset comprising diverse types of video-music pairs. Experimental results demonstrate that GVMGen surpasses previous models in terms of music-video correspondence, music quality generative diversity, and application universality.
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General Video-to-Music Generation (GVMGen) model with encoder-decoder struture. The model consists of: (1) Visual feature extraction module with spatial self-attention; (2) Feature transformation module with spatial cross-attention; (3) Conditional Music generation module with temporal cross-attention.
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Mel-spectrums of generated music by models according to the same video input in (b), (b) illustrates the pitch contours and alignment of the music generated by our model with the video input.
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<!-- Mel-spectrums of generated music by each model according to four types of video inputs. -->
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<pre><code>@inproceedings{zuo2025gvmgen,
title={GVMGen: A General Video-to-Music Generation Model With Hierarchical Attentions},
author={Zuo, heda and You, Weitao and Wu, junxian and Ren, Shihong and Chen, Pei and Zhou, Mingxu and Lu, Yujia and Sun, Lingyun},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
year={2025}
}</code></pre>
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