RGB$\leftrightarrow$X: Image decomposition and synthesis using material- and lighting-aware diffusion models
<|reference_start|>RGB$\leftrightarrow$X: Image decomposition and synthesis using material- and lighting-aware diffusion models: 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$\rightarrow$X problem. We further show that the reverse problem of synthesizing realistic images given intrinsic channels, X$\rightarrow$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$\rightarrow$X, which also estimates lighting, as well as the first diffusion X$\rightarrow$RGB model capable of synthesizing realistic images from (full or partial) intrinsic channels. Our X$\rightarrow$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 makes it possible to use a mix of heterogeneous training datasets, which 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.<|reference_end|>
@article{zeng2024rgb$\leftrightarrow$x:,
    title={RGB$\leftrightarrow$X: Image decomposition and synthesis using material-
  and lighting-aware diffusion models},
    author={Zheng Zeng, Valentin Deschaintre, Iliyan Georgiev, Yannick
  Hold-Geoffroy, Yiwei Hu, Fujun Luan, Ling-Qi Yan, Milov{s} Hav{s}an},
    journal={SIGGRAPH Conference Papers '24, July 27-August 1, 2024, Denver,
  CO, USA},
    year={2024},
    doi={10.1145/3641519.3657445},
    archivePrefix={arXiv},
    eprint={2405.00666},
    primaryClass={cs.CV cs.GR}
}