Aug 16, 2022 · In this work, we generalize continuous-time diffusion models to arbitrary Riemannian manifolds and derive a variational framework for likelihood ...
In this paper, we introduce Riemannian Diffusion Models (RDM)—generalizing conventional diffusion models on Euclidean spaces to arbitrary Riemannian manifolds.
Oct 31, 2022 · In this work, we generalize continuous-time diffusion models to arbitrary Riemannian manifolds and derive a variational framework for likelihood estimation.
Oct 30, 2023 · Riemannian diffusion models draw inspiration from standard Euclidean space diffusion models to learn distributions on general manifolds.
Apr 3, 2024 · In this work, we generalize continuous-time diffusion models to arbitrary Riemannian manifolds and derive a variational framework for likelihood ...
Riemannian diffusion models draw inspiration from standard Euclidean space diffusion models to learn distributions on general manifolds. Unfortunately, the.
May 30, 2024 · Riemannian diffusion models draw inspiration from standard Euclidean space diffusion models to learn distributions on general manifolds.
Official Code Repository for the paper Generative Modeling on Manifolds Through Mixture of Riemannian Diffusion Processes.
These models typically assume that the data geometry is flat, yet recent extensions have been developed to synthesize data living on Riemannian manifolds.