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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…

Computer Vision and Pattern Recognition · Computer Science 2026-05-19 Arslan Artykov , Tom Ravaud , Nicolás Violante-Grezzi , Vincent Lepetit

Articulated objects (e.g., doors and drawers) exist everywhere in our life. Different from rigid objects, articulated objects have higher degrees of freedom and are rich in geometries, semantics, and part functions. Modeling different kinds…

Computer Vision and Pattern Recognition · Computer Science 2023-11-22 Yushi Du , Ruihai Wu , Yan Shen , Hao Dong

We propose novel motion representations for animating articulated objects consisting of distinct parts. In a completely unsupervised manner, our method identifies object parts, tracks them in a driving video, and infers their motions by…

Computer Vision and Pattern Recognition · Computer Science 2021-04-26 Aliaksandr Siarohin , Oliver J. Woodford , Jian Ren , Menglei Chai , Sergey Tulyakov

The increasing demand for augmented reality and robotics is driving the need for articulated object reconstruction with high scalability. However, existing settings for reconstructing from discrete articulation states or casual monocular…

Computer Vision and Pattern Recognition · Computer Science 2026-03-24 Hang Dai , Hongwei Fan , Han Zhang , Duojin Wu , Jiyao Zhang , Hao Dong

We address the task of simultaneous part-level reconstruction and motion parameter estimation for articulated objects. Given two sets of multi-view images of an object in two static articulation states, we decouple the movable part from the…

Computer Vision and Pattern Recognition · Computer Science 2023-08-16 Jiayi Liu , Ali Mahdavi-Amiri , Manolis Savva

Part segmentation and motion estimation are two fundamental problems for articulated object motion analysis. In this paper, we present a method to solve these two problems jointly from a sequence of observed point clouds of a single…

Computer Vision and Pattern Recognition · Computer Science 2025-08-08 Jun-Jee Chao , Qingyuan Jiang , Volkan Isler

Building articulated objects is a key challenge in computer vision. Existing methods often fail to effectively integrate information across different object states, limiting the accuracy of part-mesh reconstruction and part dynamics…

Computer Vision and Pattern Recognition · Computer Science 2025-05-28 Yu Liu , Baoxiong Jia , Ruijie Lu , Junfeng Ni , Song-Chun Zhu , Siyuan Huang

Reconstructing articulated objects is essential for building digital twins of interactive environments. However, prior methods typically decouple geometry and motion by first reconstructing object shape in distinct states and then…

Computer Vision and Pattern Recognition · Computer Science 2025-11-13 Licheng Shen , Saining Zhang , Honghan Li , Peilin Yang , Zihao Huang , Zongzheng Zhang , Hao Zhao

Object functionality is often expressed through part articulation -- as when the two rigid parts of a scissor pivot against each other to perform the cutting function. Such articulations are often similar across objects within the same…

Computer Vision and Pattern Recognition · Computer Science 2018-09-21 Li Yi , Haibin Huang , Difan Liu , Evangelos Kalogerakis , Hao Su , Leonidas Guibas

Articulated object manipulation remains a critical challenge in robotics due to the complex kinematic constraints and the limited physical reasoning of existing methods. In this work, we introduce ArtGS, a novel framework that extends 3D…

Robotics · Computer Science 2025-07-04 Qiaojun Yu , Xibin Yuan , Yu jiang , Junting Chen , Dongzhe Zheng , Ce Hao , Yang You , Yixing Chen , Yao Mu , Liu Liu , Cewu Lu

We tackle the challenge of concurrent reconstruction at the part level with the RGB appearance and estimation of motion parameters for building digital twins of articulated objects using the 3D Gaussian Splatting (3D-GS) method. With two…

Computer Vision and Pattern Recognition · Computer Science 2025-07-08 Junfu Guo , Yu Xin , Gaoyi Liu , Kai Xu , Ligang Liu , Ruizhen Hu

Articulated objects are central to interactive 3D applications, including embodied AI, robotics, and VR/AR, where functional part decomposition and kinematic motion are essential. Yet producing high-fidelity articulated assets remains…

Computer Vision and Pattern Recognition · Computer Science 2026-02-17 Qingming Liu , Xinyue Yao , Shuyuan Zhang , Yueci Deng , Guiliang Liu , Zhen Liu , Kui Jia

Articulated objects like cabinets and doors are widespread in daily life. However, directly manipulating 3D articulated objects is challenging because they have diverse geometrical shapes, semantic categories, and kinetic constraints. Prior…

Robotics · Computer Science 2024-03-04 Qiaojun Yu , Junbo Wang , Wenhai Liu , Ce Hao , Liu Liu , Lin Shao , Weiming Wang , Cewu Lu

Many functional elements of human homes and workplaces consist of rigid components which are connected through one or more sliding or rotating linkages. Examples include doors and drawers of cabinets and appliances; laptops; and swivel…

Robotics · Computer Science 2015-02-06 Sudeep Pillai , Matthew R. Walter , Seth Teller

Articulated objects are common in the real world, yet modeling their structure and motion remains a challenging task for 3D reconstruction methods. In this work, we introduce Part$^{2}$GS, a novel framework for modeling articulated digital…

Computer Vision and Pattern Recognition · Computer Science 2026-04-10 Tianjiao Yu , Vedant Shah , Muntasir Wahed , Ying Shen , Kiet A. Nguyen , Ismini Lourentzou

We introduce Particulate, a feed-forward model that, given a 3D mesh of an object, infers its articulations, including its 3D parts, their kinematic structure, and the motion constraints. The model is based on a transformer network, the…

Computer Vision and Pattern Recognition · Computer Science 2026-03-30 Ruining Li , Yuxin Yao , Chuanxia Zheng , Christian Rupprecht , Joan Lasenby , Shangzhe Wu , Andrea Vedaldi

Building digital twins of articulated objects from monocular video presents an essential challenge in computer vision, which requires simultaneous reconstruction of object geometry, part segmentation, and articulation parameters from…

Computer Vision and Pattern Recognition · Computer Science 2025-09-23 Yu Liu , Baoxiong Jia , Ruijie Lu , Chuyue Gan , Huayu Chen , Junfeng Ni , Song-Chun Zhu , Siyuan Huang

We introduce ART, Articulated Reconstruction Transformer -- a category-agnostic, feed-forward model that reconstructs complete 3D articulated objects from only sparse, multi-state RGB images. Previous methods for articulated object…

Computer Vision and Pattern Recognition · Computer Science 2026-05-18 Zizhang Li , Cheng Zhang , Zhengqin Li , Henry Howard-Jenkins , Zhaoyang Lv , Chen Geng , Jiajun Wu , Richard Newcombe , Jakob Engel , Zhao Dong

Manipulating articulated objects with robotic arms is challenging due to the complex kinematic structure, which requires precise part segmentation for efficient manipulation. In this work, we introduce a novel superpoint-based perception…

Computer Vision and Pattern Recognition · Computer Science 2024-12-24 Qiaojun Yu , Ce Hao , Xibin Yuan , Li Zhang , Liu Liu , Yukang Huo , Rohit Agarwal , Cewu Lu

Interactive 3D simulated objects are crucial in AR/VR, animations, and robotics, driving immersive experiences and advanced automation. However, creating these articulated objects requires extensive human effort and expertise, limiting…

Computer Vision and Pattern Recognition · Computer Science 2025-06-03 Long Le , Jason Xie , William Liang , Hung-Ju Wang , Yue Yang , Yecheng Jason Ma , Kyle Vedder , Arjun Krishna , Dinesh Jayaraman , Eric Eaton
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