提升3D网格生成效率与细节，支持更长序列生成。
HiFi-Mesh: High-Fidelity Efficient 3D Mesh Generation via Compact Autoregressive Dependence
- 引入紧凑自回归依赖机制，提升生成序列长度。
- 生成速度提升6倍，可处理更大规模网格结构。
- 适合需要高精度3D建模的工业与影视应用。
高保真3D网格可被编码为一维序列，并使用自回归方法直接建模顶点和面片。然而，现有方法存在资源利用不足的问题，导致推理缓慢且仅能处理小规模序列，严重限制了结构细节的表达能力。我们提出潜空间自回归网络（LANE），在生成过程中引入紧凑的自回归依赖，使最大可生成序列长度相比现有方法提升6倍。为进一步加速推理，我们设计自适应计算图重配置（AdaGraph）策略，通过生成过程中的时空解耦有效突破传统串行推理的效率瓶颈。实验表明，LANE在生成速度、结构细节和几何一致性方面均表现优异，为高质量3D网格生成提供了有效方案。
原文摘要 · Abstract (English)
High-fidelity 3D meshes can be tokenized into one-dimension (1D) sequences and directly modeled using autoregressive approaches for faces and vertices. However, existing methods suffer from insufficient resource utilization, resulting in slow inference and the ability to handle only small-scale sequences, which severely constrains the expressible structural details. We introduce the Latent Autoregressive Network (LANE), which incorporates compact autoregressive dependencies in the generation process, achieving a $6\times$ improvement in maximum generatable sequence length compared to existing methods. To further accelerate inference, we propose the Adaptive Computation Graph Reconfiguration (AdaGraph) strategy, which effectively overcomes the efficiency bottleneck of traditional serial inference through spatiotemporal decoupling in the generation process. Experimental validation demonstrates that LANE achieves superior performance across generation speed, structural detail, and geometric consistency, providing an effective solution for high-quality 3D mesh generation.
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