Over++: Generative Video Compositing for Layer Interaction Effects
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914214922158080 |
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| author | Qi, Luchao Wu, Jiaye Choi, Jun Myeong Phillips, Cary Sengupta, Roni Goldman, Dan B |
| author_facet | Qi, Luchao Wu, Jiaye Choi, Jun Myeong Phillips, Cary Sengupta, Roni Goldman, Dan B |
| contents | In professional video compositing workflows, artists must manually create environmental interactions-such as shadows, reflections, dust, and splashes-between foreground subjects and background layers. Existing video generative models struggle to preserve the input video while adding such effects, and current video inpainting methods either require costly per-frame masks or yield implausible results. We introduce augmented compositing, a new task that synthesizes realistic, semi-transparent environmental effects conditioned on text prompts and input video layers, while preserving the original scene. To address this task, we present Over++, a video effect generation framework that makes no assumptions about camera pose, scene stationarity, or depth supervision. We construct a paired effect dataset tailored for this task and introduce an unpaired augmentation strategy that preserves text-driven editability. Our method also supports optional mask control and keyframe guidance without requiring dense annotations. Despite training on limited data, Over++ produces diverse and realistic environmental effects and outperforms existing baselines in both effect generation and scene preservation. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_19661 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Over++: Generative Video Compositing for Layer Interaction Effects Qi, Luchao Wu, Jiaye Choi, Jun Myeong Phillips, Cary Sengupta, Roni Goldman, Dan B Computer Vision and Pattern Recognition In professional video compositing workflows, artists must manually create environmental interactions-such as shadows, reflections, dust, and splashes-between foreground subjects and background layers. Existing video generative models struggle to preserve the input video while adding such effects, and current video inpainting methods either require costly per-frame masks or yield implausible results. We introduce augmented compositing, a new task that synthesizes realistic, semi-transparent environmental effects conditioned on text prompts and input video layers, while preserving the original scene. To address this task, we present Over++, a video effect generation framework that makes no assumptions about camera pose, scene stationarity, or depth supervision. We construct a paired effect dataset tailored for this task and introduce an unpaired augmentation strategy that preserves text-driven editability. Our method also supports optional mask control and keyframe guidance without requiring dense annotations. Despite training on limited data, Over++ produces diverse and realistic environmental effects and outperforms existing baselines in both effect generation and scene preservation. |
| title | Over++: Generative Video Compositing for Layer Interaction Effects |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.19661 |