arXiv Computer Vision
MeshFlow: Mesh Generation with Equivariant Flow Matching
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Balancing Symmetry and Efficiency in Graph Flow Matching
arXiv:2602. 18084v2 Announce Type: replace Abstract: Equivariance is central to graph generative models, as it ensures the model respects the permutation symmetry of graphs.
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SymTRELLIS: Symmetry-Enforced Voxel Latents for 3D Generation
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Nexus: Native Mesh Generation with Diffusion
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TriFlow: Generating Artist-Like 3D Mesh Topology via Nearest-Vertex Vector Fields
TriFlow introduces a generative method for creating compact 3D meshes with artist‑like triangle topology directly from input geometry such as signed distance fields. It represents mesh topology as a nearest‑vertex vector field (NVF) over the surface, trains a latent flow‑matching model to synthesize this field, and then clusters surface regions to guide a constrained quadric error metric simplification. The resulting meshes closely match the input geometry while exhibiting structured, artist‑like connectivity, achieving 90% lower Chamfer Distance and an 8× speedup over state‑of‑the‑art learning‑based approaches.