V-RGBX: Video Editing with Accurate Controls over Intrinsic Properties

• URL: http://arxiv.org/abs/2512.11799v1
• Date: Fri, 12 Dec 2025 18:59:54 GMT
• Title: V-RGBX: Video Editing with Accurate Controls over Intrinsic Properties
• Authors: Ye Fang, Tong Wu, Valentin Deschaintre, Duygu Ceylan, Iliyan Georgiev, Chun-Hao Paul Huang, Yiwei Hu, Xuelin Chen, Tuanfeng Yang Wang,
• Abstract summary: We present V-RGBX, the first end-to-end framework for editable video editing.<n>V-RGBX unifies three key capabilities: video inverse rendering into intrinsic channels, video synthesis from these intrinsic representations, and editable-based video editing conditioned on intrinsic channels.<n>We show that V-RGBX produces temporally consistent, photorealistic videos while propagating intrinsic appearance edits across sequences in a physically plausible manner.
• Score: 31.579053991884845
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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