SeedFACE: Rethinking Autoregressive Mesh Initialization through First-Face Modeling

Autoregressive models have shown strong potential for native 3D mesh generation, but their performance degrades on broad and diverse shape distributions. We show that this difficulty is highly non-uniform across the generation process. In particular, autoregressive mesh generation suffers from an initialization bottleneck: the first face must establish geometric context from an empty history, while later faces are strongly constrained by previously generated geometry. Prefix experiments show that providing only the first ground-truth face substantially improves generation, and further analysis reveals that the first face has the highest prediction uncertainty and becomes increasingly difficult to model as shape diversity grows. Motivated by this asymmetry, we propose SeedFACE, which separates geometric initialization from autoregressive continuation. We factor mesh generation into a seed distribution and a conditional continuation model. Rather than generating the seed face token by token from an empty context, SeedFACE represents it in a regularized latent space and learns its distribution with a lightweight latent generative model. The sampled seed is then decoded by the shared mesh decoder and serves as the geometric context for standard autoregressive generation. This preserves autoregression where geometric context is informative while avoiding token-by-token prediction in the least constrained regime. Experimental results on Objaverse demonstrate that SeedFACE substantially improves generation quality and diversity over the state-of-the-art autoregressive baselines.