Layer-Aware Video Composition via Split-then-Merge
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917105238016000 |
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| author | Kara, Ozgur Chen, Yujia Yang, Ming-Hsuan Rehg, James M. Chu, Wen-Sheng Tran, Du |
| author_facet | Kara, Ozgur Chen, Yujia Yang, Ming-Hsuan Rehg, James M. Chu, Wen-Sheng Tran, Du |
| contents | We present Split-then-Merge (StM), a novel framework designed to enhance control in generative video composition and address its data scarcity problem. Unlike conventional methods relying on annotated datasets or handcrafted rules, StM splits a large corpus of unlabeled videos into dynamic foreground and background layers, then self-composes them to learn how dynamic subjects interact with diverse scenes. This process enables the model to learn the complex compositional dynamics required for realistic video generation. StM introduces a novel transformation-aware training pipeline that utilizes a multi-layer fusion and augmentation to achieve affordance-aware composition, alongside an identity-preservation loss that maintains foreground fidelity during blending. Experiments show StM outperforms SoTA methods in both quantitative benchmarks and in humans/VLLM-based qualitative evaluations. More details are available at our project page: https://split-then-merge.github.io |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_20809 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Layer-Aware Video Composition via Split-then-Merge Kara, Ozgur Chen, Yujia Yang, Ming-Hsuan Rehg, James M. Chu, Wen-Sheng Tran, Du Computer Vision and Pattern Recognition We present Split-then-Merge (StM), a novel framework designed to enhance control in generative video composition and address its data scarcity problem. Unlike conventional methods relying on annotated datasets or handcrafted rules, StM splits a large corpus of unlabeled videos into dynamic foreground and background layers, then self-composes them to learn how dynamic subjects interact with diverse scenes. This process enables the model to learn the complex compositional dynamics required for realistic video generation. StM introduces a novel transformation-aware training pipeline that utilizes a multi-layer fusion and augmentation to achieve affordance-aware composition, alongside an identity-preservation loss that maintains foreground fidelity during blending. Experiments show StM outperforms SoTA methods in both quantitative benchmarks and in humans/VLLM-based qualitative evaluations. More details are available at our project page: https://split-then-merge.github.io |
| title | Layer-Aware Video Composition via Split-then-Merge |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.20809 |