Flow matching on homogeneous spaces
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Abstract (arXiv)
We propose a general framework to extend Flow Matching to homogeneous spaces, i.e. quotients of Lie groups. Our approach reformulates the problem as a flow matching task on the underlying Lie group by lifting the data distributions. This strategy avoids the potentially complicated geometry of homogeneous spaces by working directly on Lie groups, which in turn enables us reduce the problem to a Euclidean flow matching task on Lie algebras. In contrast to Riemannian Flow Matching, our method eliminates the need to define and compute premetrics or geodesics, resulting in a simpler, faster, and fully intrinsic framework.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 19 references that are in the map. First posted on arXiv 25 Mar 2026.
Builds on
17 of 19 references.
References · 17
- 2022Flow Matching for Generative Modelingcited by 5,426
- 2018Neural Ordinary Differential Equationscited by 3,733
- 2023Improving and generalizing flow-based generative models with minibatch optimal transportcited by 835
- 2019Augmented Neural ODEscited by 396
- 2023Flow Matching on General Geometriescited by 260
- 2020How to train your neural ODE: the world of Jacobian and kinetic regularizationcited by 228
- 2020TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamicscited by 155
- 2020OT-Flow: Fast and Accurate Continuous Normalizing Flows via Optimal Transportcited by 143
- 2023Equivariant flow matchingcited by 131
- 2020Learning Differential Equations that are Easy to Solvecited by 81
- 2024Variational Flow Matching for Graph Generationcited by 78
- 2021Moser Flow: Divergence-based Generative Modeling on Manifoldscited by 67
5 more
- 2025Exponential Family Variational Flow Matching for Tabular Data Generationcited by 15
- 2025Controlled Generation with Equivariant Variational Flow Matchingcited by 9
- 2022TO-FLOW: Efficient Continuous Normalizing Flows with Temporal Optimization adjoint with Moving Speedcited by 8
- 2020Hilbert geometry of the Siegel disk: The Siegel-Klein disk modelcited by 7
- 2025Flow Matching on Lie Groupscited by 4
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Citing papers per year: 2026: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
- 2026Cartan flow matching
- 2025Discovering Symmetry Groups with Flow Matching
- 2025Riemannian Variational Flow Matching for Material and Protein Design
- 2026Total Variation Rates for Riemannian Flow Matching
- 2025Graph Generation with Spectral Geodesic Flow Matching
- 2025Energy Guided Geometric Flow Matching
- 2025Generalised Flow Maps for Few-Step Generative Modelling on Riemannian Manifolds
- 2025Simplex-to-Euclidean Bijections for Categorical Flow Matching