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Paper · arXiv 2512.11799

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

Ye Fang, Tong Wu, Valentin Deschaintre, Duygu Ceylan, Iliyan Georgiev, Chun-Hao Paul Huang, Yiwei Hu, Xuelin Chen, Tuanfeng Yang Wang

30 upvotesDecember 12, 2025arXiv 预印本
AI 摘要

V-RGBX presents an end-to-end framework for intrinsic-aware video editing that combines video inverse rendering, photorealistic video synthesis, and keyframe-based editing with physically grounded intrinsic channel manipulation.

video inverse renderingphotorealistic video synthesiskeyframe-based video editingintrinsic channelsinterleaved conditioning mechanismintrinsic-aware video editingvideo synthesistemporal consistencyphysically plausible editing

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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