Diffusing Differentiable Representations

Yash Savani, Marc Anton Finzi, J Zico Kolter

Keywords: Diffusion models, Differential Geometry, Implicit Neural Representations, NeRF, Siren

TL;DR: We sample differentiable representations by solving the pulled back reverse diffusion process in parameter space.

Abstract: We introduce a novel, training-free method for sampling *differentiable representations* (diffreps) using pretrained diffusion models. Rather than merely mode-seeking, our method achieves sampling by "pulling back" the dynamics of the reverse-time process—from the image space to the diffrep parameter space—and updating the parameters according to this pulled-back process. We identify an implicit constraint on the samples induced by the diffrep and demonstrate that addressing this constraint significantly improves the consistency and detail of the generated objects. Our method yields diffreps with substantially **improved quality and diversity** for images, panoramas, and 3D NeRFs compared to existing techniques. Our approach is a general-purpose method for sampling diffreps, expanding the scope of problems that diffusion models can tackle.

Primary Area: Diffusion based models

Submission Number: 18771