RGB$\leftrightarrow$X: Image decomposition and synthesis using material- and lighting-aware diffusion models
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
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2024
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| author | Zeng, Zheng Deschaintre, Valentin Georgiev, Iliyan Hold-Geoffroy, Yannick Hu, Yiwei Luan, Fujun Yan, Ling-Qi Hašan, Miloš |
| author_facet | Zeng, Zheng Deschaintre, Valentin Georgiev, Iliyan Hold-Geoffroy, Yannick Hu, Yiwei Luan, Fujun Yan, Ling-Qi Hašan, Miloš |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_00666 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RGB$\leftrightarrow$X: Image decomposition and synthesis using material- and lighting-aware diffusion models Zeng, Zheng Deschaintre, Valentin Georgiev, Iliyan Hold-Geoffroy, Yannick Hu, Yiwei Luan, Fujun Yan, Ling-Qi Hašan, Miloš Computer Vision and Pattern Recognition Graphics 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. |
| title | RGB$\leftrightarrow$X: Image decomposition and synthesis using material- and lighting-aware diffusion models |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2405.00666 |