Keypoint Transformer: Solving Joint Identification in Challenging Hands and Object Interactions for Accurate 3D Pose Estimation-Reference-Cited by-同舟云学术

Keypoint Transformer: Solving Joint Identification in Challenging Hands and Object Interactions for Accurate 3D Pose Estimation

Published:2022-06 Issue: Volume: Page:
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Container-title:2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Author:

Hampali Shreyas¹,Sarkar Sayan Deb¹,Rad Mahdi¹,Lepetit Vincent²

Affiliation:

1. Institute for Computer Graphics and Vision, Graz University of Technology,Graz,Austria

2. LIGM, Ecole des Ponts, Univ Gustave Eiffel, CNRS, Marne-la-Vallée,France

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9878378/9878366/09880464.pdf?arnumber=9880464

Reference62 articles.

1. 6-DoF object pose from semantic keypoints

2. TexturePose: Supervising Human Mesh Estimation With Texture Consistency

3. Argyros. Full DOF Tracking of a Hand Interacting with an Object by Modeling Occlusions and Physical Constraints;oikonomidis;International Conference on Computer Vision,0

4. Training a Feedback Loop for Hand Pose Estimation

5. Real-Time Pose and Shape Reconstruction of Two Interacting Hands with a Single Depth Camera;mueller;IEEE Transactions on Robotics and Automation,0

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