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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/2410.22979 |
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| _version_ | 1866916912512892928 |
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| author | Zhang, Yuxin Zheng, Dandan Gong, Biao Wang, Shiwen Chen, Jingdong Yang, Ming Dong, Weiming Xu, Changsheng |
| author_facet | Zhang, Yuxin Zheng, Dandan Gong, Biao Wang, Shiwen Chen, Jingdong Yang, Ming Dong, Weiming Xu, Changsheng |
| contents | Lighting plays a pivotal role in ensuring the naturalness and aesthetic quality of video generation. However, the impact of lighting is deeply coupled with other factors of videos, e.g., objects and scenes. Thus, it remains challenging to disentangle and model coherent lighting conditions independently, limiting the flexibility to control lighting in video generation. In this paper, inspired by the established controllable T2I models, we propose LumiSculpt, which enables precise and consistent lighting control in T2V generation models. LumiSculpt equips the video generation with new interactive capabilities, allowing the input of reference image sequences with customized lighting conditions. Furthermore, the core learnable plug-and-play module of LumiSculpt facilitates direct control over the intensity, position and trajectory of an assumed light source in video diffusion models. To effectively train LumiSculpt and address the issue of insufficient lighting data, we construct LumiHuman, a new lightweight and flexible dataset for portrait lighting of images and videos. Experimental results demonstrate that LumiSculpt achieves precise and high-quality lighting control in video generation. The analysis demonstrates the flexibility of LumiHuman. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_22979 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LumiSculpt: Enabling Consistent Portrait Lighting in Video Generation Zhang, Yuxin Zheng, Dandan Gong, Biao Wang, Shiwen Chen, Jingdong Yang, Ming Dong, Weiming Xu, Changsheng Computer Vision and Pattern Recognition Lighting plays a pivotal role in ensuring the naturalness and aesthetic quality of video generation. However, the impact of lighting is deeply coupled with other factors of videos, e.g., objects and scenes. Thus, it remains challenging to disentangle and model coherent lighting conditions independently, limiting the flexibility to control lighting in video generation. In this paper, inspired by the established controllable T2I models, we propose LumiSculpt, which enables precise and consistent lighting control in T2V generation models. LumiSculpt equips the video generation with new interactive capabilities, allowing the input of reference image sequences with customized lighting conditions. Furthermore, the core learnable plug-and-play module of LumiSculpt facilitates direct control over the intensity, position and trajectory of an assumed light source in video diffusion models. To effectively train LumiSculpt and address the issue of insufficient lighting data, we construct LumiHuman, a new lightweight and flexible dataset for portrait lighting of images and videos. Experimental results demonstrate that LumiSculpt achieves precise and high-quality lighting control in video generation. The analysis demonstrates the flexibility of LumiHuman. |
| title | LumiSculpt: Enabling Consistent Portrait Lighting in Video Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.22979 |