In this paper, we introduce Topology Sculptor, Shape Refiner (TSSR), a novel method for generating high-quality, artist-style 3D meshes based on Discrete Diffusion Models (DDMs). Our primary motivation for TSSR is to achieve highly accurate token prediction while enabling parallel generation, a significant advantage over sequential autoregressive methods. By allowing TSSR to "see" all mesh tokens concurrently, we unlock a new level of efficiency and control. We leverage this parallel generation capability through three key innovations: 1) Decoupled Training and Hybrid Inference, which distinctly separates the DDM-based generation into a topology sculpting stage and a subsequent shape refinement stage. This strategic decoupling enables TSSR to effectively capture both intricate local topology and overarching global shape. 2) An Improved Hourglass Architecture, featuring bidirectional attention enriched by face-vertex-sequence level Rotational Positional Embeddings (RoPE), thereby capturing richer contextual information across the mesh structure. 3) A novel Connection Loss, which acts as a topological constraint to further enhance the realism and fidelity of the generated meshes. Extensive experiments on complex datasets demonstrate that TSSR generates high-quality 3D artist-style meshes, capable of achieving up to 10,000 faces at a remarkable spatial resolution of $1024^3$. The code will be released at: https://github.com/psky1111/Tencent-TSSR.

翻译：本文提出了一种名为拓扑雕刻师-形状精炼器（Topology Sculptor, Shape Refiner, TSSR）的新方法，该方法基于离散扩散模型（Discrete Diffusion Models, DDMs）生成高质量、艺术风格的三维网格。TSSR的主要设计动机在于实现高精度的令牌预测，同时支持并行生成，这一特性相较于序列自回归方法具有显著优势。通过允许TSSR同时“观察”所有网格令牌，我们实现了效率与控制能力的新突破。我们通过三项关键创新来利用这种并行生成能力：1）解耦训练与混合推理，将基于DDM的生成过程明确分为拓扑雕刻阶段和随后的形状精炼阶段。这种策略性解耦使TSSR能够有效捕捉精细的局部拓扑结构和整体的全局形状。2）改进的沙漏架构，采用由面-顶点-序列层级旋转位置编码（Rotational Positional Embeddings, RoPE）增强的双向注意力机制，从而捕获网格结构中更丰富的上下文信息。3）一种新颖的连接损失函数，作为拓扑约束，进一步提升生成网格的真实感与保真度。在复杂数据集上的大量实验表明，TSSR能够生成高质量的三维艺术风格网格，在高达$1024^3$的空间分辨率下，可支持多达10,000个面片的生成。代码将在以下地址发布：https://github.com/psky1111/Tencent-TSSR。