X-Part: high fidelity and structure coherent shape decomposition

• URL: http://arxiv.org/abs/2509.08643v2
• Date: Wed, 24 Sep 2025 02:57:21 GMT
• Title: X-Part: high fidelity and structure coherent shape decomposition
• Authors: Xinhao Yan, Jiachen Xu, Yang Li, Changfeng Ma, Yunhan Yang, Chunshi Wang, Zibo Zhao, Zeqiang Lai, Yunfei Zhao, Zhuo Chen, Chunchao Guo,
• Abstract summary: X-Part is a controllable generative model designed to decompose a holistic 3D object into semantically meaningful parts.<n>X-Part exploits the bounding box as prompts for the part generation and injects point-wise semantic features for meaningful decomposition.<n>This work establishes a new paradigm for creating production-ready, editable, and structurally sound 3D assets.
• Score: 21.08914191612629
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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