X-Part：高保真且结构一致的形状分解

摘要

在部件级别生成三维形状对于下游应用至关重要，如网格重拓扑、UV映射和3D打印。然而，现有的基于部件的生成方法往往缺乏足够的可控性，且语义分解效果不佳。为此，我们提出了X-Part，一种可控生成模型，旨在将整体三维对象分解为语义明确、结构连贯且几何保真度高的部件。X-Part利用边界框作为部件生成的提示，并注入点级语义特征以实现有意义的分解。此外，我们设计了一个可编辑的管道，用于交互式部件生成。大量实验结果表明，X-Part在部件级形状生成方面达到了最先进的性能。这项工作为创建生产就绪、可编辑且结构合理的3D资产建立了新范式。代码将公开发布以供研究使用。

English

Generating 3D shapes at part level is pivotal for downstream applications such as mesh retopology, UV mapping, and 3D printing. However, existing part-based generation methods often lack sufficient controllability and suffer from poor semantically meaningful decomposition. To this end, we introduce X-Part, a controllable generative model designed to decompose a holistic 3D object into semantically meaningful and structurally coherent parts with high geometric fidelity. X-Part exploits the bounding box as prompts for the part generation and injects point-wise semantic features for meaningful decomposition. Furthermore, we design an editable pipeline for interactive part generation. Extensive experimental results show that X-Part achieves state-of-the-art performance in part-level shape generation. This work establishes a new paradigm for creating production-ready, editable, and structurally sound 3D assets. Codes will be released for public research.

X-Part：高保真且结构一致的形状分解

X-Part: high fidelity and structure coherent shape decomposition

摘要

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