Part-level 3D generation is crucial for various downstream applications, including gaming, film production, and industrial design. However, decomposing a 3D shape into geometrically plausible and meaningful components remains a significant challenge. Previous part-based generation methods often struggle to produce well-constructed parts, exhibiting poor structural coherence, geometric implausibility, inaccuracy, or inefficiency. To address these challenges, we introduce EI-Part, a novel framework specifically designed to generate high-quality 3D shapes with components, characterized by strong structural coherence, geometric plausibility, geometric fidelity, and generation efficiency. We propose utilizing distinct representations at different stages: an Explode state for part completion and an Implode state for geometry refinement. This strategy fully leverages spatial resolution, enabling flexible part completion and fine geometric detail generation. To maintain structural coherence between parts, a self-attention mechanism is incorporated in both exploded and imploded states, facilitating effective information perception and feature fusion among components during generation. Extensive experiments on multiple benchmarks demonstrate that EI-Part efficiently produces semantically meaningful and structurally coherent parts with fine-grained geometric details, achieving state-of-the-art performance in part-level 3D generation. Project page: https://cvhadessun.github.io/EI-Part/

翻译：部件级三维生成对于游戏、影视制作和工业设计等下游应用至关重要。然而，将三维形状分解为几何合理且语义清晰的组件仍是一项重大挑战。现有的基于部件的生成方法往往难以构建出结构良好的部件，存在结构连贯性差、几何不合理、精度不足或效率低下等问题。为应对这些挑战，我们提出了EI-Part——一个专门设计用于生成高质量带部件三维形状的新型框架，其特点在于具有强大的结构连贯性、几何合理性、几何保真度以及生成效率。我们提出在不同阶段采用差异化表征：利用爆裂状态进行部件补全，借助内聚状态实现几何细化。该策略充分挖掘空间分辨率优势，支持灵活的部件补全与精细几何细节生成。为保持部件间的结构连贯性，我们在爆裂与内聚状态中均引入了自注意力机制，从而在生成过程中促进组件间的高效信息感知与特征融合。在多个基准数据集上的大量实验表明，EI-Part能够高效生成语义明确、结构连贯且具有细粒度几何细节的部件，在部件级三维生成任务中达到了最先进的性能水平。项目页面：https://cvhadessun.github.io/EI-Part/