用自回归模型生成人类风格的3D几何构件组合，更贴近人眼对形状的理解。
PrimitiveAnything: Human-Crafted 3D Primitive Assembly Generation with Auto-Regressive Transformer
- 将3D形状分解任务转为构件逐步组装，用Transformer自动生成
- 在多种形状类别上生成结果更符合人类感知且保持几何精度
- 适合游戏等需要用户创作3D构件的应用场景
形状基元抽象将复杂3D形状分解为简单几何元素，在人类视觉认知与计算机视觉、图形学中具有重要意义。尽管近期3D内容生成取得显著进展，现有基元抽象方法或依赖几何优化而语义理解有限，或基于小规模、类别特定数据集训练，难以跨类别泛化。我们提出PrimitiveAnything，将形状基元抽象重新定义为基元组装生成任务。该框架包含一个形状条件化的基元Transformer用于自回归生成，以及一种无歧义的参数化方案，统一表示多种类型基元。模型直接从大规模人工设计的抽象数据中学习基元组装过程，捕捉人类如何将复杂形状拆解为基元。大量实验表明，PrimitiveAnything能生成高质量基元组合，更符合人类感知且在多样形状类别中保持几何保真度。该方法可赋能多种3D应用，并具备推动游戏中基于基元的用户生成内容（UGC）的潜力。
原文摘要 · Abstract (English)
Shape primitive abstraction, which decomposes complex 3D shapes into simple geometric elements, plays a crucial role in human visual cognition and has broad applications in computer vision and graphics. While recent advances in 3D content generation have shown remarkable progress, existing primitive abstraction methods either rely on geometric optimization with limited semantic understanding or learn from small-scale, category-specific datasets, struggling to generalize across diverse shape categories. We present PrimitiveAnything, a novel framework that reformulates shape primitive abstraction as a primitive assembly generation task. PrimitiveAnything includes a shape-conditioned primitive transformer for auto-regressive generation and an ambiguity-free parameterization scheme to represent multiple types of primitives in a unified manner. The proposed framework directly learns the process of primitive assembly from large-scale human-crafted abstractions, enabling it to capture how humans decompose complex shapes into primitive elements. Through extensive experiments, we demonstrate that PrimitiveAnything can generate high-quality primitive assemblies that better align with human perception while maintaining geometric fidelity across diverse shape categories. It benefits various 3D applications and shows potential for enabling primitive-based user-generated content (UGC) in games. Project page: https://primitiveanything.github.io
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