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Diffusion models currently achieve state-of-the-art performance for bothconditional and unconditional image generation. However, so far, imagediffusion models do not support tasks required for 3D understanding, such asview-consistent 3D generation or single-view object reconstruction. In thispaper, we present RenderDiffusion, the first diffusion model for 3D generationand inference, trained using only monocular 2D supervision. Central to ourmethod is a novel image denoising architecture that generates and renders anintermediate three-dimensional representation of a scene in each denoisingstep. This enforces a strong inductive structure within the diffusion process,providing a 3D consistent representation while only requiring 2D supervision.The resulting 3D representation can be rendered from any view. We evaluateRenderDiffusion on FFHQ, AFHQ, ShapeNet and CLEVR datasets, showing competitiveperformance for generation of 3D scenes and inference of 3D scenes from 2Dimages. Additionally, our diffusion-based approach allows us to use 2Dinpainting to edit 3D scenes.

RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and Generation