FACE: A Face-based Autoregressive Representation for High-Fidelity and Efficient Mesh Generation
Hanxiao Wang, Yuan-Chen Guo, Ying-Tian Liu, Zi-Xin Zou, Biao Zhang, Weize Quan, Ding Liang, Yan-Pei Cao, Dong-Ming Yan
Autoregressive mesh generation is traditionally bottlenecked by the quadratic compute cost of modeling flattened vertex coordinate sequences. FACE fundamentally resolves this by elevating generation to a higher semantic tier. Through a novel “one-face-one-token” strategy, we encapsulate entire triangle faces into single unified tokens, slashing sequence lengths by a factor of nine. This architectural elegance yields an unprecedented compression ratio of 0.11, doubling the efficiency of prior state-of-the-art without relying on brittle, lossy traversal heuristics. By coupling this highly efficient Autoregressive Autoencoder (ARAE) with latent diffusion, FACE provides a robust, scalable, and compute-efficient foundation for high-fidelity direct mesh generation.
2026