BEAT: Rhythm-Elastic Alignment for Agentic Music-guided Movie Trailer Generation
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| Format: | Preprint |
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2026
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| _version_ | 1866911720396554240 |
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| author | Wang, Yutong Wang, Yunke Chen, Xinyuan Xu, Chang |
| author_facet | Wang, Yutong Wang, Yunke Chen, Xinyuan Xu, Chang |
| contents | Automatic movie trailer generation must select shots from a full-length film and synchronize them with background music. Existing methods either relegate music alignment to post-processing or enforce rigid one-to-one shot-music mappings, overlooking that professional editing rhythm is elastic: rapid cuts accompany high-energy passages while sustained shots span quieter bars. We introduce BEAT, a framework that addresses this gap with two core components: MuVA, a compact music-visual alignment encoder trained with Sinkhorn-regularized two-stage learning, and Bar-DP, an energy-adaptive dynamic programming algorithm that produces elastic many-to-one alignments following musical dynamics. These components are integrated into a five-phase agentic pipeline that grounds the core alignment in learned cross-modal features while coordinating higher-level creative decisions through structured text signals. To support comprehensive evaluation, we also introduce TrailerArena, a benchmark with 20+ metrics across four complementary dimensions. On TrailerArena, BEAT achieves state-of-the-art performance across shot selection, ordering, and perceptual quality, while producing fully composed trailers end-to-end. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_27067 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | BEAT: Rhythm-Elastic Alignment for Agentic Music-guided Movie Trailer Generation Wang, Yutong Wang, Yunke Chen, Xinyuan Xu, Chang Computer Vision and Pattern Recognition Automatic movie trailer generation must select shots from a full-length film and synchronize them with background music. Existing methods either relegate music alignment to post-processing or enforce rigid one-to-one shot-music mappings, overlooking that professional editing rhythm is elastic: rapid cuts accompany high-energy passages while sustained shots span quieter bars. We introduce BEAT, a framework that addresses this gap with two core components: MuVA, a compact music-visual alignment encoder trained with Sinkhorn-regularized two-stage learning, and Bar-DP, an energy-adaptive dynamic programming algorithm that produces elastic many-to-one alignments following musical dynamics. These components are integrated into a five-phase agentic pipeline that grounds the core alignment in learned cross-modal features while coordinating higher-level creative decisions through structured text signals. To support comprehensive evaluation, we also introduce TrailerArena, a benchmark with 20+ metrics across four complementary dimensions. On TrailerArena, BEAT achieves state-of-the-art performance across shot selection, ordering, and perceptual quality, while producing fully composed trailers end-to-end. |
| title | BEAT: Rhythm-Elastic Alignment for Agentic Music-guided Movie Trailer Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.27067 |