FACE: A Face-based Autoregressive Representation for High-Fidelity and Efficient Mesh Generation

• URL: http://arxiv.org/abs/2603.01515v2
• Date: Tue, 03 Mar 2026 10:12:47 GMT
• Title: FACE: A Face-based Autoregressive Representation for High-Fidelity and Efficient Mesh Generation
• Authors: Hanxiao Wang, Yuan-Chen Guo, Ying-Tian Liu, Zi-Xin Zou, Biao Zhang, Weize Quan, Ding Liang, Yan-Pei Cao, Dong-Ming Yan,
• Abstract summary: We introduce FACE, a novel Autoregressive Autoencoder framework that generates meshes at the face level.<n>Our one-face-one-token strategy treats each triangle face, the fundamental building block of a mesh, as a single, unified token.<n> FACE achieves state-of-the-art reconstruction quality on standard benchmarks.
• Score: 50.71369329585773
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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