ArtFormer: 多様な3D関節物体の制御可能な生成
ArtFormer: Controllable Generation of Diverse 3D Articulated Objects
関節物体をトークンの木として表現し、Transformerで形状コードと運動学関係を生成、SDFで各部位を高品質に合成する手法を提案。
著者: Jiayi Su, Youhe Feng, Zheng Li, Jinhua Song, Yangfan He, Botao Ren, Botian Xu
分類: cs.CV, cs.AI, cs.RO
原文アブストラクト
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.
