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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.10822 |
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| _version_ | 1866917570671542272 |
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| author | Pan, Boxiao Xu, Zhan Huang, Chun-Hao Paul Singh, Krishna Kumar Zhou, Yang Guibas, Leonidas J. Yang, Jimei |
| author_facet | Pan, Boxiao Xu, Zhan Huang, Chun-Hao Paul Singh, Krishna Kumar Zhou, Yang Guibas, Leonidas J. Yang, Jimei |
| contents | Generating video background that tailors to foreground subject motion is an important problem for the movie industry and visual effects community. This task involves synthesizing background that aligns with the motion and appearance of the foreground subject, while also complies with the artist's creative intention. We introduce ActAnywhere, a generative model that automates this process which traditionally requires tedious manual efforts. Our model leverages the power of large-scale video diffusion models, and is specifically tailored for this task. ActAnywhere takes a sequence of foreground subject segmentation as input and an image that describes the desired scene as condition, to produce a coherent video with realistic foreground-background interactions while adhering to the condition frame. We train our model on a large-scale dataset of human-scene interaction videos. Extensive evaluations demonstrate the superior performance of our model, significantly outperforming baselines. Moreover, we show that ActAnywhere generalizes to diverse out-of-distribution samples, including non-human subjects. Please visit our project webpage at https://actanywhere.github.io. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_10822 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ActAnywhere: Subject-Aware Video Background Generation Pan, Boxiao Xu, Zhan Huang, Chun-Hao Paul Singh, Krishna Kumar Zhou, Yang Guibas, Leonidas J. Yang, Jimei Computer Vision and Pattern Recognition Generating video background that tailors to foreground subject motion is an important problem for the movie industry and visual effects community. This task involves synthesizing background that aligns with the motion and appearance of the foreground subject, while also complies with the artist's creative intention. We introduce ActAnywhere, a generative model that automates this process which traditionally requires tedious manual efforts. Our model leverages the power of large-scale video diffusion models, and is specifically tailored for this task. ActAnywhere takes a sequence of foreground subject segmentation as input and an image that describes the desired scene as condition, to produce a coherent video with realistic foreground-background interactions while adhering to the condition frame. We train our model on a large-scale dataset of human-scene interaction videos. Extensive evaluations demonstrate the superior performance of our model, significantly outperforming baselines. Moreover, we show that ActAnywhere generalizes to diverse out-of-distribution samples, including non-human subjects. Please visit our project webpage at https://actanywhere.github.io. |
| title | ActAnywhere: Subject-Aware Video Background Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.10822 |