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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2512.11799 |
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| _version_ | 1866911316135903232 |
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| author | Fang, Ye Wu, Tong Deschaintre, Valentin Ceylan, Duygu Georgiev, Iliyan Huang, Chun-Hao Paul Hu, Yiwei Chen, Xuelin Wang, Tuanfeng Yang |
| author_facet | Fang, Ye Wu, Tong Deschaintre, Valentin Ceylan, Duygu Georgiev, Iliyan Huang, Chun-Hao Paul Hu, Yiwei Chen, Xuelin Wang, Tuanfeng Yang |
| contents | Large-scale video generation models have shown remarkable potential in modeling photorealistic appearance and lighting interactions in real-world scenes. However, a closed-loop framework that jointly understands intrinsic scene properties (e.g., albedo, normal, material, and irradiance), leverages them for video synthesis, and supports editable intrinsic representations remains unexplored. We present V-RGBX, the first end-to-end framework for intrinsic-aware video editing. V-RGBX unifies three key capabilities: (1) video inverse rendering into intrinsic channels, (2) photorealistic video synthesis from these intrinsic representations, and (3) keyframe-based video editing conditioned on intrinsic channels. At the core of V-RGBX is an interleaved conditioning mechanism that enables intuitive, physically grounded video editing through user-selected keyframes, supporting flexible manipulation of any intrinsic modality. Extensive qualitative and quantitative results show that V-RGBX produces temporally consistent, photorealistic videos while propagating keyframe edits across sequences in a physically plausible manner. We demonstrate its effectiveness in diverse applications, including object appearance editing and scene-level relighting, surpassing the performance of prior methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_11799 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | V-RGBX: Video Editing with Accurate Controls over Intrinsic Properties Fang, Ye Wu, Tong Deschaintre, Valentin Ceylan, Duygu Georgiev, Iliyan Huang, Chun-Hao Paul Hu, Yiwei Chen, Xuelin Wang, Tuanfeng Yang Computer Vision and Pattern Recognition Large-scale video generation models have shown remarkable potential in modeling photorealistic appearance and lighting interactions in real-world scenes. However, a closed-loop framework that jointly understands intrinsic scene properties (e.g., albedo, normal, material, and irradiance), leverages them for video synthesis, and supports editable intrinsic representations remains unexplored. We present V-RGBX, the first end-to-end framework for intrinsic-aware video editing. V-RGBX unifies three key capabilities: (1) video inverse rendering into intrinsic channels, (2) photorealistic video synthesis from these intrinsic representations, and (3) keyframe-based video editing conditioned on intrinsic channels. At the core of V-RGBX is an interleaved conditioning mechanism that enables intuitive, physically grounded video editing through user-selected keyframes, supporting flexible manipulation of any intrinsic modality. Extensive qualitative and quantitative results show that V-RGBX produces temporally consistent, photorealistic videos while propagating keyframe edits across sequences in a physically plausible manner. We demonstrate its effectiveness in diverse applications, including object appearance editing and scene-level relighting, surpassing the performance of prior methods. |
| title | V-RGBX: Video Editing with Accurate Controls over Intrinsic Properties |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.11799 |