Equivariant Latent Alignment via Flow Matching under Group Symmetries 事件

Equivariant Latent Alignment via Flow Matching under Group Symmetries arXiv:2605.30705v1 Announce Type: new Abstract: Geometry-aware generative models and novel view synthesis approaches have shown strong potential in visual fidelity and consistency. In parallel, equivariant representation learning has emerged as a powerful framework for constructing latent spaces where analytically known group transformations could act directly, capturing geometric structure in data and enhancing both interpre