Command-driven Articulated Object Understanding and Manipulation

We present Cart, a new approach towards articulatedobject manipulations by human commands. Beyond the existing work that focuses on inferring articulation structures, we further support manipulating articulated shapes to align them subject to simple command templates. The key of Cart is to utilize t...

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Bibliographic Details
Published in2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp. 8813 - 8823
Main Authors Chu, Ruihang, Liu, Zhengzhe, Ye, Xiaoqing, Tan, Xiao, Qi, Xiaojuan, Fu, Chi-Wing, Jia, Jiaya
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2023
Subjects
3D from multi-view and sensors
Codes
Computer vision
Encoding
Geometry
Pattern recognition
Shape
Visualization
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Summary:We present Cart, a new approach towards articulatedobject manipulations by human commands. Beyond the existing work that focuses on inferring articulation structures, we further support manipulating articulated shapes to align them subject to simple command templates. The key of Cart is to utilize the prediction of object structures to connect visual observations with user commands for effective manipulations. It is achieved by encoding command messages for motion prediction and a test-time adaptation to adjust the amount of movement from only command supervision. For a rich variety of object categories, Cart can accurately manipulate object shapes and outperform the state-of-the-art approaches in understanding the inherent articulation structures. Also, it can well generalize to unseen object categories and real-world objects. We hope Cart could open new directions for instructing machines to operate articulated objects. Code is available at https://github.com/dvlab-research/Cart.
ISSN:2575-7075
DOI:10.1109/CVPR52729.2023.00851