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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.07076 |
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| _version_ | 1866916784586620928 |
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| author | Wu, Xinyi Wang, Haohong Katsaggelos, Aggelos K. |
| author_facet | Wu, Xinyi Wang, Haohong Katsaggelos, Aggelos K. |
| contents | With the popularity of video-based user-generated content (UGC) on social media, harmony, as dictated by human perceptual principles, is critical in assessing the rhythmic consistency of audio-visual UGCs for better user engagement. In this work, we propose a novel harmony-aware GAN framework, following a specifically designed harmony evaluation strategy to enhance rhythmic synchronization in the automatic music-to-motion synthesis using a UGC dance dataset. This harmony strategy utilizes refined cross-modal beat detection to capture closely correlated audio and visual rhythms in an audio-visual pair. To mimic human attention mechanism, we introduce saliency-based beat weighting and interval-driven beat alignment, which ensures accurate harmony score estimation consistent with human perception. Building on this strategy, our model, employing efficient encoder-decoder and depth-lifting designs, is adversarially trained based on categorized musical meter segments to generate realistic and rhythmic 3D human motions. We further incorporate our harmony evaluation strategy as a weakly supervised perceptual constraint to flexibly guide the synchronized audio-visual rhythms during the generation process. Experimental results show that our proposed model significantly outperforms other leading music-to-motion methods in rhythmic harmony, both quantitatively and qualitatively, even with limited UGC training data. Live samples 15 can be watched at: https://youtu.be/tWwz7yq4aUs |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_07076 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Harmony-Aware Music-driven Motion Synthesis with Perceptual Constraint on UGC Datasets Wu, Xinyi Wang, Haohong Katsaggelos, Aggelos K. Multimedia With the popularity of video-based user-generated content (UGC) on social media, harmony, as dictated by human perceptual principles, is critical in assessing the rhythmic consistency of audio-visual UGCs for better user engagement. In this work, we propose a novel harmony-aware GAN framework, following a specifically designed harmony evaluation strategy to enhance rhythmic synchronization in the automatic music-to-motion synthesis using a UGC dance dataset. This harmony strategy utilizes refined cross-modal beat detection to capture closely correlated audio and visual rhythms in an audio-visual pair. To mimic human attention mechanism, we introduce saliency-based beat weighting and interval-driven beat alignment, which ensures accurate harmony score estimation consistent with human perception. Building on this strategy, our model, employing efficient encoder-decoder and depth-lifting designs, is adversarially trained based on categorized musical meter segments to generate realistic and rhythmic 3D human motions. We further incorporate our harmony evaluation strategy as a weakly supervised perceptual constraint to flexibly guide the synchronized audio-visual rhythms during the generation process. Experimental results show that our proposed model significantly outperforms other leading music-to-motion methods in rhythmic harmony, both quantitatively and qualitatively, even with limited UGC training data. Live samples 15 can be watched at: https://youtu.be/tWwz7yq4aUs |
| title | Harmony-Aware Music-driven Motion Synthesis with Perceptual Constraint on UGC Datasets |
| topic | Multimedia |
| url | https://arxiv.org/abs/2506.07076 |