FastMesh:Efficient Artistic Mesh Generation via Component Decoupling
Recent mesh generation approaches typically tokenize triangle meshes into
sequences of tokens and train autoregressive models to generate these tokens
sequentially. Despite substantial progress, such token sequences inevitably
reuse vertices multiple times to fully represent manifold meshes, as each
vertex is shared by multiple faces. This redundancy leads to excessively long
token sequences and inefficient generation processes. In this paper, we propose
an efficient framework that generates artistic meshes by treating vertices and
faces separately, significantly reducing redundancy. We employ an
autoregressive model solely for vertex generation, decreasing the token count
to approximately 23\% of that required by the most compact existing tokenizer.
Next, we leverage a bidirectional transformer to complete the mesh in a single
step by capturing inter-vertex relationships and constructing the adjacency
matrix that defines the mesh faces. To further improve the generation quality,
we introduce a fidelity enhancer to refine vertex positioning into more natural
arrangements and propose a post-processing framework to remove undesirable edge
connections. Experimental results show that our method achieves more than
8times faster speed on mesh generation compared to state-of-the-art
approaches, while producing higher mesh quality.
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Jeonghwan Kim
claimed_verified
2025-08-28T08:56:30.319Z
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Jeonghwan Kim
user
WopperSet
68ae75e7364411bea07df74f
Yushi Lan
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2025-08-29T09:12:16.701Z
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/avatars/d5fc53de37d4fb0b5095a5fb21b43503.svg
LAN YUSHI
user
yslan
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Armando Fortes
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2025-08-27T12:45:42.330Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/63d6ffc644f1d8fbe5901f7d/oHXMSjp8Oi5ROSBU919hc.jpeg
Armando Fortes
user
atfortes
68ae75e7364411bea07df751
Yongwei Chen
68ae75e7364411bea07df752
Xingang Pan
FastMesh:Efficient Artistic Mesh Generation via Component Decoupling
Recent mesh generation approaches typically tokenize triangle meshes into
sequences of tokens and train autoregressive models to generate these tokens
sequentially. Despite substantial progress, such token sequences inevitably
reuse vertices multiple times to fully represent manifold meshes, as each
vertex is shared by multiple faces. This redundancy leads to excessively long
token sequences and inefficient generation processes. In this paper, we propose
an efficient framework that generates artistic meshes by treating vertices and
faces separately, significantly reducing redundancy. We employ an
autoregressive model solely for vertex generation, decreasing the token count
to approximately 23\% of that required by the most compact existing tokenizer.
Next, we leverage a bidirectional transformer to complete the mesh in a single
step by capturing inter-vertex relationships and constructing the adjacency
matrix that defines the mesh faces. To further improve the generation quality,
we introduce a fidelity enhancer to refine vertex positioning into more natural
arrangements and propose a post-processing framework to remove undesirable edge
connections. Experimental results show that our method achieves more than
8times faster speed on mesh generation compared to state-of-the-art
approaches, while producing higher mesh quality.
A framework for efficient artistic mesh generation reduces redundancy by separating vertex and face generation, using an autoregressive model for vertices and a bidirectional transformer for faces, and includes a fidelity enhancer and post-processing to improve quality and speed.
autoregressive models bidirectional transformer vertex generation face generation fidelity enhancer post-processing mesh generation adjacency matrix mesh quality