Flow Matching on General Geometries
This addresses generative modeling on complex geometries for applications in fields like computer graphics or physics, representing a novel method rather than an incremental improvement.
The authors tackled the problem of training continuous normalizing flows on manifolds by proposing Riemannian Flow Matching (RFM), which bypasses limitations like expensive simulation and biased objectives, achieving state-of-the-art performance on real-world non-Euclidean datasets.
We propose Riemannian Flow Matching (RFM), a simple yet powerful framework for training continuous normalizing flows on manifolds. Existing methods for generative modeling on manifolds either require expensive simulation, are inherently unable to scale to high dimensions, or use approximations for limiting quantities that result in biased training objectives. Riemannian Flow Matching bypasses these limitations and offers several advantages over previous approaches: it is simulation-free on simple geometries, does not require divergence computation, and computes its target vector field in closed-form. The key ingredient behind RFM is the construction of a relatively simple premetric for defining target vector fields, which encompasses the existing Euclidean case. To extend to general geometries, we rely on the use of spectral decompositions to efficiently compute premetrics on the fly. Our method achieves state-of-the-art performance on many real-world non-Euclidean datasets, and we demonstrate tractable training on general geometries, including triangular meshes with highly non-trivial curvature and boundaries.