Meshy T2: Fast Native Mesh Generation with Flow Matching

Hugging Face Daily Papers Papers
mesh-generation flow-matching 3d vae generative-model image-to-mesh

Summary

Meshy T2 introduces a fast native mesh generation framework using flow matching and a vertex-set mesh VAE, achieving state-of-the-art geometric fidelity with end-to-end image-to-mesh generation in a median of 6 seconds, over an order of magnitude faster than autoregressive baselines.

Polygonal meshes are the standard surface representation of modern 3D pipelines, and generating high-quality meshes with artist-style topology is essential for film, gaming, and interactive 3D applications. Mainstream approaches serialize a mesh into a token sequence and decode it autoregressively, which is slow at inference and sensitive to error accumulation, making them impractical for interactive asset creation. We present Meshy T2, a fast native mesh generation framework built on flow matching. At its core is a vertex-set mesh VAE that encodes a mesh into one continuous latent token per vertex and decodes vertices, edge connectivity, and face winding order in a single pass, preserving high-precision geometry and artist-authored topology without vertex quantization or welding. Generation proceeds as a coarse-to-fine cascade of two flow-matching models: an image-conditioned voxel flow first sketches the overall shape as a coarse occupancy scaffold, and a mesh flow then populates the scaffold with per-vertex latent tokens, conditioned on the image, the scaffold, and a requested vertex budget. This design delivers three practical capabilities: interactive generation speed through parallel flow-based synthesis; effective face-count control through the requested vertex budget; and native support for multi-part assets, whose components emerge directly from the generated connectivity. In our experiments, Meshy T2 achieves state-of-the-art geometric fidelity and completes end-to-end image-to-mesh generation within a median of 6 seconds, over an order of magnitude faster than autoregressive baselines. Code and weights will be available at https://github.com/meshy-dev/meshy-t2.
Original Article
View Cached Full Text

Cached at: 08/03/26, 05:30 AM

Paper page - Meshy T2: Fast Native Mesh Generation with Flow Matching

Source: https://huggingface.co/papers/2607.28675

Abstract

Polygonalmeshesarethestandardsurfacerepresentationofmodern3Dpipelines,andgeneratinghigh-qualitymesheswithartist-styletopologyisessentialforfilm,gaming,andinteractive3Dapplications.Mainstreamapproachesserializeameshintoatokensequenceanddecodeitautoregressively,whichisslowatinferenceandsensitivetoerroraccumulation,makingthemimpracticalforinteractiveassetcreation.WepresentMeshyT2,afastnativemeshgenerationframeworkbuiltonflowmatching.Atitscoreisavertex-setmeshVAEthatencodesameshintoonecontinuouslatenttokenpervertexanddecodesvertices,edgeconnectivity,andfacewindingorderinasinglepass,preservinghigh-precisiongeometryandartist-authoredtopologywithoutvertexquantizationorwelding.Generationproceedsasacoarse-to-finecascadeoftwoflow-matchingmodels:animage-conditionedvoxelflowfirstsketchestheoverallshapeasacoarseoccupancyscaffold,andameshflowthenpopulatesthescaffoldwithper-vertexlatenttokens,conditionedontheimage,thescaffold,andarequestedvertexbudget.Thisdesigndeliversthreepracticalcapabilities:interactivegenerationspeedthroughparallelflow-basedsynthesis;effectiveface-countcontrolthroughtherequestedvertexbudget;andnativesupportformulti-partassets,whosecomponentsemergedirectlyfromthegeneratedconnectivity.Inourexperiments,MeshyT2achievesstate-of-the-artgeometricfidelityandcompletesend-to-endimage-to-meshgenerationwithinamedianof6seconds,overanorderofmagnitudefasterthanautoregressivebaselines.Codeandweightswillbeavailableathttps://github.com/meshy-dev/meshy-t2.

View arXiv pageView PDFAdd to collection

Get this paper in your agent:

hf papers read 2607\.28675

Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash

Models citing this paper0

No model linking this paper

Cite arxiv.org/abs/2607.28675 in a model README.md to link it from this page.

Datasets citing this paper0

No dataset linking this paper

Cite arxiv.org/abs/2607.28675 in a dataset README.md to link it from this page.

Spaces citing this paper0

No Space linking this paper

Cite arxiv.org/abs/2607.28675 in a Space README.md to link it from this page.

Collections including this paper0

No Collection including this paper

Add this paper to acollectionto link it from this page.

Similar Articles

MeshFlow: Mesh Generation with Equivariant Flow Matching

Hugging Face Daily Papers

MeshFlow introduces an equivariant optimal-transport flow matching model for direct triangle mesh generation, achieving state-of-the-art quality while providing approximately 18x inference speedup over autoregressive methods.

LATO.2: Factorized 3D Mesh Generation with Vertex and Topology Flow

Hugging Face Daily Papers

LATO.2 is a factorized flow matching framework for 3D mesh generation that decomposes the process into a vertex flow followed by a connectivity flow conditioned on the vertices, achieving state-of-the-art geometric fidelity and connectivity quality.

Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing

Hugging Face Daily Papers

Mage-Flow is a compact 4B-parameter generative stack for efficient text-to-image generation and instruction-based image editing, featuring a co-designed lightweight tokenizer (Mage-VAE) and a native-resolution multimodal diffusion transformer trained with rectified flow matching. It achieves competitive performance while enabling high-resolution generation at 0.59s on a single A100 GPU.