Mesh generation is of great value in various applications involving computer graphics and virtual content, yet designing generative models for meshes is challenging due to their irregular data structure and inconsistent topology of meshes in the same category. In this work, we design a novel sparse latent point diffusion model for mesh generation. Our key insight is to regard point clouds as an intermediate representation of meshes, and model the distribution of point clouds instead. While meshes can be generated from point clouds via techniques like Shape as Points (SAP), the challenges of directly generating meshes can be effectively avoided. To boost the efficiency and controllability of our mesh generation method, we propose to further encode point clouds to a set of sparse latent points with point-wise semantic meaningful features, where two DDPMs are trained in the space of sparse latent points to respectively model the distribution of the latent point positions and features at these latent points. We find that sampling in this latent space is faster than directly sampling dense point clouds. Moreover, the sparse latent points also enable us to explicitly control both the overall structures and local details of the generated meshes. Extensive experiments are conducted on the ShapeNet dataset, where our proposed sparse latent point diffusion model achieves superior performance in terms of generation quality and controllability when compared to existing methods.

翻译：网格生成在涉及计算机图形学和虚拟内容的各类应用中具有重要价值，但由于网格数据的不规则结构以及同一类别中网格拓扑的不一致性，设计用于网格的生成模型颇具挑战。在本工作中，我们设计了一种新颖的稀疏潜在点扩散模型用于网格生成。我们的关键思路是将点云视为网格的中间表示，转而建模点云的分布。虽然可以通过"形状即点"（Shape as Points, SAP）等技术从点云生成网格，但直接生成网格的挑战可被有效规避。为提升网格生成方法的效率和可控性，我们提出进一步将点云编码为一组具有逐点语义有意义特征的稀疏潜在点，并在稀疏潜在点空间训练两个DDPM，分别建模潜在点位置和这些潜点特征的分布。我们发现，在该潜在空间中采样比直接采样密集点云更快。此外，稀疏潜在点还使我们能够显式控制生成网格的整体结构和局部细节。我们在ShapeNet数据集上进行了大量实验，结果表明，与现有方法相比，我们提出的稀疏潜在点扩散模型在生成质量和可控性方面均取得了更优性能。