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This paper presents a novel approach to inpainting 3D regions of a scene,given masked multi-view images, by distilling a 2D diffusion model into alearned 3D scene representation (e.g. a NeRF). Unlike 3D generative methodsthat explicitly condition the diffusion model on camera pose or multi-viewinformation, our diffusion model is conditioned only on a single masked 2Dimage. Nevertheless, we show that this 2D diffusion model can still serve as agenerative prior in a 3D multi-view reconstruction problem where we optimize aNeRF using a combination of score distillation sampling and NeRF reconstructionlosses. Predicted depth is used as additional supervision to encourage accurategeometry. We compare our approach to 3D inpainting methods that focus on objectremoval. Because our method can generate content to fill any 3D masked region,we additionally demonstrate 3D object completion, 3D object replacement, and 3Dscene completion.

Inpaint3D: 3D Scene Content Generation using 2D Inpainting Diffusion