Look Both Ways: Future-Informed Autoregressive High-Poly Mesh Generation

Existing part-wise autoregressive 3D mesh generation methods predict each next part conditioned only on preceding parts. Without explicit future-informed topological compatibility guidance, early local topological biases can accumulate along the generation chain and eventually lead to topological drift in high-face-count settings. We propose FAMesh, a topologically consistent framework for high-face-count 3D mesh generation. FAMesh formulates target mesh generation as part-wise autoregressive prediction of topological sequences and introduces a global topological scaffold (TopoScaf), which captures the coarse global structure of the target mesh as a compact part-level topological sequence. TopoScaf complements the target-part autoregression with explicit cues about future-part topological compatibility, steering generation along a topologically consistent trajectory. Furthermore, we design Part-Structured Asymmetric Interaction (PSAI), which reformulates information transfer required for TopoScaf modeling and TopoScaf-to-target topological guidance into efficient interactions over discriminative part-level structural summaries. This design achieves a $1.52\times$ training speedup, without compromising TopoScaf modeling fidelity or its topological guidance for target generation. Experiments show that FAMesh outperforms existing methods in generation quality and topological consistency.