# MeshCarve

MeshCarve introduces a flow matching method that runs entirely in spatially compressed latents: a VAE generates coarse vertex occupancy in one forward pass as an anchor, then a vertex flow and an edge flow generate vertex positions and edge connections separately, with both latents learned by a VertexVAE and an EdgeVAE sharing a hierarchical sparse transformer backbone with spatial-aware compression and vertex-link encoding; its latent holds 0.13 tokens per face on average, achieving the lowest Chamfer distance and highest normal consistency on Objaverse and generalizing to Toys4K.

MeshCarve introduces a flow matching method that runs entirely in spatially compressed latents: a VAE generates coarse vertex occupancy in one forward pass as an anchor, then a vertex flow and an edge flow generate vertex positions and edge connections separately, with both latents learned by a VertexVAE and an EdgeVAE sharing a hierarchical sparse transformer backbone with spatial-aware compression and vertex-link encoding; its latent holds 0.13 tokens per face on average, achieving the lowest Chamfer distance and highest normal consistency on Objaverse and generalizing to Toys4K.