Autoregressive models for 3D mesh generation suffer from a fundamental limitation: they flatten meshes into long vertex-coordinate sequences. This results in prohibitive computational costs, hindering the efficient synthesis of high-fidelity geometry. We argue this bottleneck stems from operating at the wrong semantic level. We introduce FACE, a novel Autoregressive Autoencoder (ARAE) framework that reconceptualizes the task by generating meshes at the face level. Our one-face-one-token strategy treats each triangle face, the fundamental building block of a mesh, as a single, unified token. This simple yet powerful design reduces the sequence length by a factor of nine, leading to an unprecedented compression ratio of 0.11, halving the previous state-of-the-art. This dramatic efficiency gain does not compromise quality; by pairing our face-level decoder with a powerful VecSet encoder, FACE achieves state-of-the-art reconstruction quality on standard benchmarks. The versatility of the learned latent space is further demonstrated by training a latent diffusion model that achieves high-fidelity, single-image-to-mesh generation. FACE provides a simple, scalable, and powerful paradigm that lowers the barrier to high-quality structured 3D content creation.
Figures & tables
Figure 2: The end-to-end pipeline of our FACE model. An encoder compresses the input point cloud into a latent VecSet. An autoregressive decoder then conditions on this VecSet, generating the mesh face-by-face. A Face Embedding layer encoder 9 tokens of each face and a CausalMLP head decodes each latent face token into 9 quantized coordinate tokens.
Method
MeshXL
MeshAnything
MeshGPT
PivotMesh
EdgeRunner
MeshAnything v2
DeepMesh
Nautilus
BPT
Mesh-Silksong
TreeMeshGPT
Ours
Compression Ratio ↓
1.00
1.00
0.67
0.67
0.47
0.46
0.28
0.27
0.26
0.22
0.22
0.11
Table 1: Comparison on mesh token efficiency. Our method achieves a state-of-the-art compression ratio, representing the mesh with the shortest sequence length.
Figure 3: Overview of our image-to-mesh generation pipeline. We first use the input image to condition a DiT model. The resulting latent VecSet is then fed into the Autoregressive Face Decoder to produce the final mesh.
Objaverse
Toys4K
Famous
Method
Hausdorff ↓
Chamfer ↓
Hausdorff ↓
Chamfer ↓
Hausdorff ↓
Chamfer ↓
MeshAnything
0.327
0.128
0.264
0.110
0.399
0.177
MeshAnythingV2
0.265
0.089
0.238
0.090
0.488
0.203
TreeMeshGPT
0.183
0.055
0.133
0.047
0.226
0.064
BPT
0.126
0.043
0.091
0.037
0.143
0.061
Ours (FACE)
0.090
0.041
0.067
0.033
0.077
0.049
Table 2: Quantitative comparison of mesh reconstruction quality on multiple datasets.
Figure 4: Qualitative comparison of mesh reconstruction quality on the Toys4K dataset.
Figure 5: Qualitative comparison of image-conditioned mesh generation.
Method (Order)
Hausdorff Distance ↓
Chamfer Distance ↓
BFS
0.728
0.528
DFS
0.171
0.077
ZYX-component
0.110
0.045
ZYX
0.103
0.047
Table 3: Ablation on mesh face ordering strategies.
Queries
Hausdorff Distance ↓
Chamfer Distance ↓
Learnable
0.132
0.058
Downsample
0.103
0.047
Table 4: Ablation on different queries of the shape encoder.
Method
Hausdorff Distance ↓
Chamfer Distance ↓
Parallel Decode
0.426
0.239
Attention-based
0.132
0.064
CausalMLP
0.103
0.047
Table 5: Ablation on coordinate decoding strategies.
Figure 6: Qualitative comparisons between the base and large model.
Autoregressive mesh generation has gained attention by tokenizing meshes into sequences and training models in a language-modeling fashion. However, existing approaches suffer from two fundamental limitations: (i) low tokenization efficiency, which yields long token sequences and prevents scaling to high-poly meshes, and (ii) absence of geometry-aware guidance, as generation is conditioned only on global shape embeddings rather than local surface cues. We introduce MeshWeaver, an autoregressive framework that treats mesh generation as a surface weaving process by directly predicting the next vertex instead of independent coordinates. At its core is a multi-level sparse-voxel encoder that injects geometric context into the generative process in three complementary ways: providing voxel features as vertex representations, guiding token prediction via cross-attention to voxel features, and serving as a structural scaffold that constrains generation around the input surface. Our hierarchical design enables coarse-to-fine vertex prediction in a single decoding step, while tightly coupling the generative model with 3D geometry. Extensive experiments demonstrate that MeshWeaver achieves a state-of-the-art compression ratio of 18%, can generate meshes with up to 16K faces, and significantly improves geometric fidelity over prior approaches.
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.
We present MeshFlow, a new method for generating artist-like 3D meshes. Current mesh generators often adopt Auto-Regressive (AR) next-token prediction, a natural choice given the discrete nature of mesh topology. However, AR methods scale poorly because the inference cost is quadratic in mesh size. They also require discretizing the vertex coordinates, which introduces quantization errors. To address these challenges, we introduce a Variational Autoencoder (VAE) that, supervised with a contrastive loss, represents both continuous vertex positions and discrete connectivity in a continuous latent space. This latent space is significantly more compact than prior token-based mesh representations. We then build a 3D generator based on a Rectified Flow transformer, generating all mesh vertices and edges in parallel. Our model generates meshes 18x faster than the fastest AR generator while also achieving excellent accuracy across standard mesh-generation metrics. Homepage: https://mesh-flow.github.io/, Code: https://github.com/facebookresearch/meshflow
Weiyu Li, Antoine Toisoul, Tom Monnier +4
Meta AI · Hong Kong University of Science and Technology