Equivariant Latent Alignment via Flow Matching under Group Symmetries

ArXi:2605.30705v1 Announce Type: cross 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 interpretability and generalization in novel view synthesis.