SDF 1
♻ ☆ ArtFormer: Controllable Generation of Diverse 3D Articulated Objects CVPR 2025
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.
comment: CVPR 2025. impl. repo: https://github.com/ShuYuMo2003/ArtFormer