X-Part：高保真且结构一致的形状分解

摘要

在部件層面生成3D形狀對於網格重拓撲、UV映射和3D打印等下游應用至關重要。然而，現有的基於部件的生成方法往往缺乏足夠的可控性，並且在語義上有意義的分解方面表現不佳。為此，我們引入了X-Part，這是一種可控生成模型，旨在將整體3D對象分解為語義上有意義且結構連貫的部件，並具有高幾何保真度。X-Part利用邊界框作為部件生成的提示，並注入點級語義特徵以實現有意義的分解。此外，我們設計了一個可編輯的管道，用於交互式部件生成。大量實驗結果表明，X-Part在部件層面形狀生成方面達到了最先進的性能。這項工作為創建生產就緒、可編輯且結構合理的3D資產建立了新範式。代碼將公開以供研究使用。

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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