分部件扩散生成3D网格，兼顾全局结构与局部细节。
PartDiffuser: Part-wise 3D Mesh Generation via Discrete Diffusion
- 分部件生成：部件间自回归保全局结构，部件内并行扩散复现高频细节。
- 相比顶尖模型，生成网格细节更丰富，适合真实场景应用。
- 适用于需要高精度3D建模的工业设计、游戏开发等场景。
现有自回归方法在生成艺术家设计的网格时难以兼顾全局结构一致性与高保真局部细节，且易出现误差累积。为此，我们提出PartDiffuser，一种基于离散扩散的半自回归框架，用于点云到网格的生成。该方法首先对网格进行语义分割，随后采用“分部件”策略：在部件间使用自回归确保全局拓扑，在每个语义部件内采用并行离散扩散过程精确重建高频几何特征。PartDiffuser基于DiT架构，引入部件感知的交叉注意力机制，以点云作为分层几何条件动态控制生成过程，有效解耦全局与局部生成任务。实验表明，该方法在生成富含细节的3D网格方面显著优于当前最先进（SOTA）模型，展现出极佳的细节表现力，适用于真实世界应用。
原文摘要 · Abstract (English)
Existing autoregressive (AR) methods for generating artist-designed meshes struggle to balance global structural consistency with high-fidelity local details, and are susceptible to error accumulation. To address this, we propose PartDiffuser, a novel semi-autoregressive diffusion framework for point-cloud-to-mesh generation. The method first performs semantic segmentation on the mesh and then operates in a "part-wise" manner: it employs autoregression between parts to ensure global topology, while utilizing a parallel discrete diffusion process within each semantic part to precisely reconstruct high-frequency geometric features. PartDiffuser is based on the DiT architecture and introduces a part-aware cross-attention mechanism, using point clouds as hierarchical geometric conditioning to dynamically control the generation process, thereby effectively decoupling the global and local generation tasks. Experiments demonstrate that this method significantly outperforms state-of-the-art (SOTA) models in generating 3D meshes with rich detail, exhibiting exceptional detail representation suitable for real-world applications.
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