Articulation in Prime: Primitive-Based Articulated Object Understanding from a Single Casual Video

• URL: http://arxiv.org/abs/2605.18645v1
• Date: Mon, 18 May 2026 16:52:41 GMT
• Title: Articulation in Prime: Primitive-Based Articulated Object Understanding from a Single Casual Video
• Abstract summary: Retrieving 3D kinematics of articulated objects from monocular video is a fundamental challenge in computer vision.<n>We propose a category-agnostic optimization framework that treats articulated object understanding as a primitive-fitting problem.<n>We also propose the AiP-synth and AiP-real benchmarks, featuring significant camera motion and heavy occlusions, and outperform existing methods.
• Score: 18.06685620402906
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieving the 3D kinematics of articulated objects from monocular video is a fundamental challenge in computer vision. Existing methods rely on complex video setups or cues such as long-term point tracking or wide-baseline matching, but are frequently brittle under severe occlusions, rapid camera ego-motion, or weak local features. Learning-based methods, meanwhile, struggle to generalize beyond their training categories. We propose a category-agnostic optimization framework that treats articulated object understanding as a primitive-fitting problem. Geometric primitives serve as a proxy representation that avoids the pitfalls of unstable point tracks; a novel mechanism organizes them into coherent parts constrained by revolute and prismatic joints. Our formulation jointly optimizes part segmentation and joint parameters, recovering complex kinematics from a single casually captured video. A visibility-aware procedure handles partial observations and occlusions inherent to real-world data. We also propose the AiP-synth and AiP-real benchmarks, featuring significant camera motion and heavy occlusions, and outperform existing methods. Project page: https://aartykov.github.io/Articulation-in-Prime/

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