V-RGBX: Video Editing with Accurate Controls over Intrinsic Properties
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 39 of its 49 references that are in the map. First posted on arXiv 12 Dec 2025.
Builds on
39 of 49 references.
References · 39
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
- 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
- 2022Scalable Diffusion Models with Transformerscited by 6,054
- 2022Classifier-Free Diffusion Guidancecited by 5,847
- 2025Wan: Open and Advanced Large-Scale Video Generative Modelscited by 2,708
- 2023Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasetscited by 2,581
- 2022Video Diffusion Modelscited by 2,305
- 2024CogVideoX: Text-to-Video Diffusion Models with An Expert Transformercited by 2,261
- 2023VBench: Comprehensive Benchmark Suite for Video Generative Modelscited by 1,822
- 2022Make-A-Video: Text-to-Video Generation without Text-Video Datacited by 1,742
27 more
- 2024HunyuanVideo: A Systematic Framework For Large Video Generative Modelscited by 1,660
- 2023Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Modelscited by 1,476
- 2023AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuningcited by 1,433
- 2023MotionCtrl: A Unified and Flexible Motion Controller for Video Generationcited by 580
- 2017MoCoGAN: Decomposing Motion and Content for Video Generationcited by 524
- 2025VACE: All-in-One Video Creation and Editingcited by 479
- 2024CameraCtrl: Enabling Camera Control for Text-to-Video Generationcited by 460
- 2024Lumiere: A Space-Time Diffusion Model for Video Generationcited by 455
- 2024ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesiscited by 403
- 2022Latent Video Diffusion Models for High-Fidelity Long Video Generationcited by 388
- 2015Deep multi-scale video prediction beyond mean square errorcited by 300
- 2025ReCamMaster: Camera-Controlled Generative Rendering from A Single Videocited by 287
- 2025Diffusion as Shader: 3D-aware Video Diffusion for Versatile Video Generation Controlcited by 185
- 2018Stochastic Adversarial Video Predictioncited by 173
- 2024RGBleftrightarrowX: Image decomposition and synthesis using material- and lighting-aware diffusion modelscited by 161
- 2024VD3D: Taming Large Video Diffusion Transformers for 3D Camera Controlcited by 156
- 2024AnyV2V: A Tuning-Free Framework For Any Video-to-Video Editing Taskscited by 154
- 2024AC3D: Analyzing and Improving 3D Camera Control in Video Diffusion Transformerscited by 154
- 2025Video World Models with Long-term Spatial Memorycited by 136
- 2024SynCamMaster: Synchronizing Multi-Camera Video Generation from Diverse Viewpointscited by 93
- 2023DiffusionLight: Light Probes for Free by Painting a Chrome Ballcited by 64
- 2024ARLON: Boosting Diffusion Transformers with Autoregressive Models for Long Video Generationcited by 41
- 2024Generative Video Propagationcited by 36
- 2025IntrinsicEdit: Precise generative image manipulation in intrinsic spacecited by 17
- 2025PRISM: A Unified Framework for Photorealistic Reconstruction and Intrinsic Scene Modelingcited by 9
- 2025Teamwork: Collaborative Diffusion with Low-rank Coordination and Adaptationcited by 3
- 2025X2Video: Adapting Diffusion Models for Multimodal Controllable Neural Video Renderingcited by 3
Built on by
Citing papers per year: 2026: 4.
4 papers.
- 2026Relit-LiVE: Relight Video by Jointly Learning Environment Videocited by 1
- 2026RGBX-Next: Towards Realistic Generative Rendering from G-Bufferscited by 1
- 2026PIXLRelight: Controllable Relighting via Intrinsic Conditioningcited by 0
- 2026AlbedoEdit: Unified Instance-Level Video Editing with Albedo Guidancecited by 0
- 2026RGBX-Next: Towards Realistic Generative Rendering from G-Buffers14 Aug 2026
- 2026AlbedoEdit: Unified Instance-Level Video Editing with Albedo Guidance31 May 2026
- 2026PIXLRelight: Controllable Relighting via Intrinsic Conditioning18 May 2026
- 2026Relit-LiVE: Relight Video by Jointly Learning Environment Video7 May 2026