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Variational Prediction
Alexander A. Alemi, Ben Poole
Bayesian inference offers benefits over maximum likelihood, but it also comes with computational costs. Computing the posterior is typically intractable, as is marginalizing that p…
Diffusion Self-Guidance for Controllable Image Generation
Dave Epstein, Allan Jabri, Ben Poole +2
Large-scale generative models are capable of producing high-quality images from detailed text descriptions. However, many aspects of an image are difficult or impossible to convey…
Learning a Diffusion Prior for NeRFs
Guandao Yang, Abhijit Kundu, Leonidas J. Guibas +2
Neural Radiance Fields (NeRFs) have emerged as a powerful neural 3D representation for objects and scenes derived from 2D data. Generating NeRFs, however, remains difficult in many…
DreamBooth3D: Subject-Driven Text-to-3D Generation
Amit Raj, Srinivas Kaza, Ben Poole +9
We present DreamBooth3D, an approach to personalize text-to-3D generative models from as few as 3-6 casually captured images of a subject. Our approach combines recent advances in…