RGB↔X: Image decomposition and synthesis using material- and lighting-aware diffusion models

SIGGRAPH 2024 (conference)

We present models for image decomposition into intrinsic channels (RGB→X) and image synthesis from such channels (RGB→X) in a unified conditional diffusion framework. (a) Our RGB→X model produces clean, plausible estimates of the intrinsic channels X. (b) New realistic images can be produced using our RGB→X model. Here we use a subset of the estimated channels, plus a text prompt. (c) We insert synthetic objects into the estimated channels and use an in-painting version of our RGB→X model, with appropriate masks, to synthesize a final composite image with matching lighting and shadows.


The three areas of realistic forward rendering, per-pixel inverse rendering, and generative image synthesis may seem like separate and unrelated sub-fields of graphics and vision. However, recent work has demonstrated improved estimation of per-pixel intrinsic channels (albedo, roughness, metallicity) based on a diffusion architecture; we call this the RGB→X problem. We further show that the reverse problem of synthesizing realistic images given intrinsic channels, X→RGB, can also be addressed in a diffusion framework. Focusing on the image domain of interior scenes, we introduce an improved diffusion model for RGB→X, which also estimates lighting, as well as the first diffusion X→RGB model capable of synthesizing realistic images from (full or partial) intrinsic channels. Our X→RGB model explores a middle ground between traditional rendering and generative models: We can specify only certain appearance properties that should be followed, and give freedom to the model to hallucinate a plausible version of the rest. This flexibility allows using a mix of heterogeneous training datasets that differ in the available channels. We use multiple existing datasets and extend them with our own synthetic and real data, resulting in a model capable of extracting scene properties better than previous work and of generating highly realistic images of interior scenes.

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BibTeX reference

  author = {Zheng Zeng and Valentin Deschaintre and Iliyan Georgiev and Yannick Hold-Geoffroy and Yiwei Hu and Fujun Luan and Ling-Qi Yan and Miloš Hašan},
  title = {RGB↔X: Image decomposition and synthesis using material- and lighting-aware diffusion models},
  booktitle = {ACM SIGGRAPH 2024 Conference Proceedings},
  year = {2024},
  doi = {10.1145/3641519.3657445},
  isbn = {979-8-4007-0525-0/24/07}