LayerFlow: A Unified Model for Layer-aware Video Generation
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909637221023744 |
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| author | Ji, Sihui Luo, Hao Chen, Xi Tu, Yuanpeng Wang, Yiyang Zhao, Hengshuang |
| author_facet | Ji, Sihui Luo, Hao Chen, Xi Tu, Yuanpeng Wang, Yiyang Zhao, Hengshuang |
| contents | We present LayerFlow, a unified solution for layer-aware video generation. Given per-layer prompts, LayerFlow generates videos for the transparent foreground, clean background, and blended scene. It also supports versatile variants like decomposing a blended video or generating the background for the given foreground and vice versa. Starting from a text-to-video diffusion transformer, we organize the videos for different layers as sub-clips, and leverage layer embeddings to distinguish each clip and the corresponding layer-wise prompts. In this way, we seamlessly support the aforementioned variants in one unified framework. For the lack of high-quality layer-wise training videos, we design a multi-stage training strategy to accommodate static images with high-quality layer annotations. Specifically, we first train the model with low-quality video data. Then, we tune a motion LoRA to make the model compatible with static frames. Afterward, we train the content LoRA on the mixture of image data with high-quality layered images along with copy-pasted video data. During inference, we remove the motion LoRA thus generating smooth videos with desired layers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04228 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LayerFlow: A Unified Model for Layer-aware Video Generation Ji, Sihui Luo, Hao Chen, Xi Tu, Yuanpeng Wang, Yiyang Zhao, Hengshuang Computer Vision and Pattern Recognition We present LayerFlow, a unified solution for layer-aware video generation. Given per-layer prompts, LayerFlow generates videos for the transparent foreground, clean background, and blended scene. It also supports versatile variants like decomposing a blended video or generating the background for the given foreground and vice versa. Starting from a text-to-video diffusion transformer, we organize the videos for different layers as sub-clips, and leverage layer embeddings to distinguish each clip and the corresponding layer-wise prompts. In this way, we seamlessly support the aforementioned variants in one unified framework. For the lack of high-quality layer-wise training videos, we design a multi-stage training strategy to accommodate static images with high-quality layer annotations. Specifically, we first train the model with low-quality video data. Then, we tune a motion LoRA to make the model compatible with static frames. Afterward, we train the content LoRA on the mixture of image data with high-quality layered images along with copy-pasted video data. During inference, we remove the motion LoRA thus generating smooth videos with desired layers. |
| title | LayerFlow: A Unified Model for Layer-aware Video Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.04228 |