This paper presents a novel framework for modeling and conditional generation of 3D articulated objects. Troubled by flexibility-quality tradeoffs, existing methods are often limited to using predefined structures or retrieving shapes from static datasets. To address these challenges, we parameterize an articulated object as a tree of tokens and employ a transformer to generate both the object's high-level geometry code and its kinematic relations. Subsequently, each sub-part's geometry is further decoded using a signed-distance-function (SDF) shape prior, facilitating the synthesis of high-quality 3D shapes. Our approach enables the generation of diverse objects with high-quality geometry and varying number of parts. Comprehensive experiments on conditional generation from text descriptions demonstrate the effectiveness and flexibility of our method.

翻译：本文提出了一种新颖的三维关节化物体建模与条件生成框架。现有方法受限于灵活性-质量权衡，通常只能使用预定义结构或从静态数据集中检索形状。为应对这些挑战，我们将关节化物体参数化为树形令牌结构，并采用Transformer同时生成物体的高层几何编码及其运动学关系。随后，每个子部件的几何形状通过带符号距离函数（SDF）形状先验进行解码，从而促进高质量三维形状的合成。本方法能够生成具有高质量几何形态且部件数量可变的多样化物体。基于文本描述的条件生成综合实验验证了本方法的有效性与灵活性。