EI-Part：爆炸以补全，内爆以精修
EI-Part:Explode for Completion and Implode for Refinement
提出 Explode/Implode 双状态表示与自注意力跨部件融合，为部件级 3D 生成提供可复用的分阶段生成框架，可迁移至可控编辑与装配式建模。
原文依据 We propose utilizing distinct representations at different stages: an Explode state for part completion and an Implode state for geometry refinement.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论部件级三维生成对游戏、电影制作和工业设计等多种下游应用至关重要。然而，将三维形状分解为几何上合理且有意义的组件仍是一项重大挑战。以往的基于部件的生成方法往往难以生成构造良好的部件，表现出结构连贯性差、几何不合理、不准确或效率低下等问题。为解决这些挑战，我们提出 EI-Part，一种新颖的框架，专门用于生成具有结构连贯性、几何合理性、准确性和生成效率的高质量三维形状及其组件。我们提出在不同阶段使用不同的表示：用于部件补全的爆炸状态和用于几何精修的内爆状态。该策略使我们能够充分利用空间分辨率，实现灵活的部件补全和精细几何细节生成。为保持部件间的结构连贯性，在爆炸状态和内爆状态下均引入了自注意力机制，促进生成过程中组件间的有效信息感知和特征融合。在各种基准上进行的广泛实验表明，EI-Part 能够高效地生成具有细粒度几何细节的语义有意义且结构连贯的部件，与现有方法相比，在部件级生成方面达到了最优性能。
英文原文摘要 1485 字符
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 either 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 distinguished by structural coherence, geometric plausibility, accuracy, 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 allows us to fully leverage spatial resolution, enabling flexible part completion and fine geometric details generation. To maintain structural coherence between parts, a self-attention mechanism is incorporated in both the exploded and imploded states, facilitating effective information perception and feature fusion among components during generation. Extensive experiments conducted on various benchmarks demonstrate that EI-Part efficiently yields semantically meaningful and structurally coherent parts with fine-grained geometric details, achieving state-of-the-art performance in part-level generation compared to existing methods.