Kinematify: Open-Vocabulary Synthesis of High-DoF Articulated Objects
Authors: name: Jiawei Wang, name: Dingyou Wang, name: Jiaming Hu, name: Qixuan Zhang, name: Jingyi Yu, name: Lan Xu
Organization: DEEMOS Technology

A deep understanding of kinematic structures and movable components is
essential for enabling robots to manipulate objects and model their own
articulated forms. Such understanding is captured through articulated objects,
which are essential for tasks such as physical simulation, motion planning, and
policy learning. However, creating these models, particularly for objects with
high degrees of freedom (DoF), remains a significant challenge. Existing
methods typically rely on motion sequences or strong assumptions from
hand-curated datasets, which hinders scalability. In this paper, we introduce
Kinematify, an automated framework that synthesizes articulated objects
directly from arbitrary RGB images or textual descriptions. Our method
addresses two core challenges: (i) inferring kinematic topologies for high-DoF
objects and (ii) estimating joint parameters from static geometry. To achieve
this, we combine MCTS search for structural inference with geometry-driven
optimization for joint reasoning, producing physically consistent and
functionally valid descriptions. We evaluate Kinematify on diverse inputs from
both synthetic and real-world environments, demonstrating improvements in
registration and kinematic topology accuracy over prior work.