Kinematify: Open-Vocabulary Synthesis of High-DoF Articulated Objects
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.
690c12a360494e4fa7675647
Jiawei Wang
claimed_verified
2025-11-06T11:48:05.263Z
677c3d4cae40dca500232ae2
https://cdn-avatars.huggingface.co/v1/production/uploads/677c3d4cae40dca500232ae2/1L8-cOO5vNz9uqNmlJbqH.png
Joel Wang
jwjoel
user
jwjoel
690c12a360494e4fa7675648
Dingyou Wang
690c12a360494e4fa7675649
Jiaming Hu
690c12a360494e4fa767564a
Qixuan Zhang
690c12a360494e4fa767564b
Jingyi Yu
690c12a360494e4fa767564c
Lan Xu
Kinematify: Open-Vocabulary Synthesis of High-DoF Articulated Objects
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.
Kinematify is an automated framework that synthesizes articulated objects from RGB images or textual descriptions, addressing challenges in inferring kinematic topologies and estimating joint parameters.
articulated objects motion planning policy learning degrees of freedom MCTS search geometry-driven optimization registration kinematic topology accuracy