X-Part: high fidelity and structure coherent shape decomposition
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.
68c23e6829b8ec9932cd0974
Xinhao Yan
claimed_verified
2025-09-12T16:13:12.403Z
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Xinhao Yan
user
HowieYan
68c23e6829b8ec9932cd0975
Jiachen Xu
68c23e6829b8ec9932cd0976
Yang Li
68c23e6829b8ec9932cd0977
Changfeng Ma
68c23e6829b8ec9932cd0978
Yunhan Yang
68c23e6829b8ec9932cd0979
Chunshi Wang
68c23e6829b8ec9932cd097a
Zibo Zhao
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Zeqiang Lai
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Yunfei Zhao
68c23e6829b8ec9932cd097d
Zhuo Chen
68c23e6829b8ec9932cd097e
Chunchao Guo
X-Part: high fidelity and structure coherent shape decomposition
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 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.
generative model part-level shape generation bounding box point-wise semantic features interactive part generation