用Transformer生成可控制的多样化3D可动物体，兼顾质量与灵活性。
ArtFormer: Controllable Generation of Diverse 3D Articulated Objects
- 将物体建模为分层令牌树，用Transformer联合生成几何与运动关系。
- 支持多部件、高质量3D形状生成，能根据文本描述进行条件生成。
- 适合需要多样且精细3D可动模型的场景，如动画与游戏设计。
本文提出一种新型框架，用于建模与条件生成3D可动物体。现有方法常受限于结构预定义或静态数据集，面临灵活性与质量的权衡。为此，我们把可动物体参数化为令牌树结构，并利用Transformer生成物体的高层几何编码与运动关系。随后，通过符号距离函数（SDF）形状先验解码各子部件几何，实现高质量3D形状合成。该方法支持生成具有不同部件数量和高保真度的多样化3D物体。在基于文本描述的条件生成任务上，大量实验验证了方法的有效性与灵活性。
原文摘要 · Abstract (English)
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.
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