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We present Viewset Diffusion, a diffusion-based generator that outputs 3Dobjects while only using multi-view 2D data for supervision. We note that thereexists a one-to-one mapping between viewsets, i.e., collections of several 2Dviews of an object, and 3D models. Hence, we train a diffusion model togenerate viewsets, but design the neural network generator to reconstructinternally corresponding 3D models, thus generating those too. We fit adiffusion model to a large number of viewsets for a given category of objects.The resulting generator can be conditioned on zero, one or more input views.Conditioned on a single view, it performs 3D reconstruction accounting for theambiguity of the task and allowing to sample multiple solutions compatible withthe input. The model performs reconstruction efficiently, in a feed-forwardmanner, and is trained using only rendering losses using as few as three viewsper viewset. Project page: szymanowiczs.github.io/viewset-diffusion.

Viewset Diffusion: (0-)Image-Conditioned 3D Generative Models from 2D Data