HGA3D: High-Fidelity Geometry-Aware 3D Generation Acceleration

Recent VecSet-based 3D diffusion models, which represent 3D geometry as a compact set of latent vectors, achieve high generation quality but suffer from prohibitive inference latency due to multi-step denoising and expensive decoding. The central challenge is identifying reducible computational redundancy without corrupting the zero-level surface that determines the final mesh. To address this issue, we leverage geometry preservation as a guiding principle to accelerate 3D generation, implementing acceleration strategies across both the diffusion and decoding phases. In the diffusion stage, we introduce geometry-aware token pruning, which exploits decoder cross-attention between sparse dynamic surface anchors and latent tokens to retain surface-critical tokens while eliminating redundancy. In the decoding stage, we propose curvature-adaptive volume decoding, which facilitates efficient mesh decoding by adaptively concentrating high-resolution queries on geometrically complex regions while sparsifying redundant queries in smooth near-surface areas. Experiments on TripoSG and Hunyuan3D-2.0 demonstrate total inference speedups of $1.69\times$ and $1.49\times$, respectively, while preserving high-fidelity and structurally consistent 3D generation. Our code will be released.