Track2Art: Motion-Centric Articulated Object Model Recovery from 2D Point Trackers

2026-09-23

AI Summary

该研究通过Track2Art框架，利用持续运动的点轨迹来恢复结构化的关节对象模型，解决了从2D追踪器中准确发现刚性部分及其运动关系的问题。

Abstract

Understanding articulated objects is fundamental for robotic interaction, requiring accurate rigid-part discovery and the recovery of their kinematic relations. Existing approaches often treat articulation as a by-product of reconstructed geometry or recover it through per-instance optimization. We instead build on the hypothesis that articulation is directly observable from persistent motion: points on the same rigid part move coherently, while relative motion between parts reveals their kinematic constraints. We present Track2Art, a motion-centric framework for recovering structured articulated objects from RGB-D interaction videos. Track2Art lifts tracked image points into persistent 3D trajectories and combines pretrained tracking features, visual descriptors, and explicit trajectory geometry. These representations are grouped into a variable number of rigid-part hypotheses and subsequently used to recover directed kinematic relations, joint types, and joint geometry through rotation-equivariant learned--analytic reasoning. On the aligned 20-object PartNet-Mobility suite, Track2Art achieves 0.695 Point IoU and 0.410 end-to-end J@20, while requiring neither ground-truth part counts nor test-time optimization.

Problem

• articulated objects
• rigid-part discovery
• kinematic relations

Innovation

• motion-centric
• articulated object recovery
• rotation-equivariant reasoning
• persistent 3D trajectories