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We will open-source the code soon. Here is the project homepage: https://yanxinhao.github.io/Projects/X-Part/

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Papers
arxiv:2509.08643

X-Part: high fidelity and structure coherent shape decomposition

Published on Sep 10
· Submitted by Xinhao Yan on Sep 15
Authors:
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Abstract

X-Part is a generative model that decomposes 3D objects into semantically meaningful parts with high fidelity, using bounding boxes and point-wise semantic features, and supports interactive editing.

AI-generated summary

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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Paper author Paper submitter
edited 14 days ago

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We will open-source the code soon. Here is the project homepage: https://yanxinhao.github.io/Projects/X-Part/

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