X-Part: high fidelity and structure coherent shape decomposition
Authors: name: Xinhao Yan, name: Jiachen Xu, name: Yang Li, name: Changfeng Ma, name: Yunhan Yang, name: Chunshi Wang, name: Zibo Zhao, name: Zeqiang Lai, name: Yunfei Zhao, name: Zhuo Chen, name: Chunchao Guo

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.