Kinematify：高自由度铰接物体的开放词汇合成

摘要

对运动学结构与可动部件的深入理解，对于实现机器人操控物体及模拟自身关节形态至关重要。这种认知通过关节化对象得以体现，其在物理仿真、运动规划及策略学习等任务中具有核心价值。然而针对高自由度物体的建模工作仍面临重大挑战。现有方法通常依赖运动序列或人工标注数据集中的强假设，这限制了方法的扩展性。本文提出Kinematify框架，能够直接从任意RGB图像或文本描述自动生成关节化对象。我们的方法攻克了两大核心难题：(i)推断高自由度物体的运动学拓扑结构；(ii)从静态几何形态中估计关节参数。通过结合蒙特卡洛树搜索进行结构推断，以及基于几何驱动的优化方法进行关节推理，本框架可生成物理一致且功能有效的运动学描述。我们在合成环境与真实场景的多样化输入上评估Kinematify，结果表明其在配准精度与运动学拓扑准确性方面均优于现有方法。

English

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：高自由度铰接物体的开放词汇合成

Kinematify: Open-Vocabulary Synthesis of High-DoF Articulated Objects

摘要

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