172 citations · 1.1k across the 55 of their papers we have counts for
10 papers · 1 filter
Guided Flows for Generative Modeling and Decision Making
Qinqing Zheng, Matt Le, Neta Shaul +3
Classifier-free guidance is a key component for enhancing the performance of conditional generative models across diverse tasks. While it has previously demonstrated remarkable imp…
Mosaic-SDF for 3D Generative Models
Lior Yariv, Omri Puny, Natalia Neverova +2
Current diffusion or flow-based generative models for 3D shapes divide to two: distilling pre-trained 2D image diffusion models, and training directly on 3D shapes. When training a…
Bespoke Solvers for Generative Flow Models
Neta Shaul, Juan Perez, Ricky T. Q. Chen +3
Diffusion or flow-based models are powerful generative paradigms that are notoriously hard to sample as samples are defined as solutions to high-dimensional Ordinary or Stochastic…
Generalized Schrödinger Bridge Matching
Guan-Horng Liu, Yaron Lipman, Maximilian Nickel +3
Modern distribution matching algorithms for training diffusion or flow models directly prescribe the time evolution of the marginal distributions between two boundary distributions…
On Kinetic Optimal Probability Paths for Generative Models
Neta Shaul, Ricky T. Q. Chen, Maximilian Nickel +2
Recent successful generative models are trained by fitting a neural network to an a-priori defined tractable probability density path taking noise to training examples. In this pap…
Multisample Flow Matching: Straightening Flows with Minibatch Couplings
Aram-Alexandre Pooladian, Heli Ben-Hamu, Carles Domingo-Enrich +3
Simulation-free methods for training continuous-time generative models construct probability paths that go between noise distributions and individual data samples. Recent works, su…
VisCo Grids: Surface Reconstruction with Viscosity and Coarea Grids
Albert Pumarola, Artsiom Sanakoyeu, Lior Yariv +2
Surface reconstruction has been seeing a lot of progress lately by utilizing Implicit Neural Representations (INRs). Despite their success, INRs often introduce hard to control ind…
MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation
Omer Bar-Tal, Lior Yariv, Yaron Lipman +1
Recent advances in text-to-image generation with diffusion models present transformative capabilities in image quality. However, user controllability of the generated image, and fa…
Equivariant Polynomials for Graph Neural Networks
Omri Puny, Derek Lim, Bobak T. Kiani +2
Graph Neural Networks (GNN) are inherently limited in their expressive power. Recent seminal works (Xu et al., 2019; Morris et al., 2019b) introduced the Weisfeiler-Lehman (WL) hie…
Flow Matching on General Geometries
Ricky T. Q. Chen, Yaron Lipman
We propose Riemannian Flow Matching (RFM), a simple yet powerful framework for training continuous normalizing flows on manifolds. Existing methods for generative modeling on manif…