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Paper Abstract and Keywords
Presentation 2026-03-25 09:20
Building a pose estimation model based on the hierarchical structure using the transformer decoder
Kong Huijing, Makoto Murakami (Toyo univ) PRMU2025-54
Abstract (in Japanese) (See Japanese page) 
(in English) In conventional human pose estimation methods based on neural networks, changes in the type or number of joints require modifications to the network architecture, making it impossible to handle different pose datasets with a single model. In this study, we reformulate the pose estimation task as a regression problem that predicts a variable-length set of joint positions, enabling a single model to process different types of pose data. Humans are capable of estimating the poses of various articulated objects, such as animals and mechanical manipulators, and this ability is thought to stem from modeling the hierarchical structure of joints itself. Inspired by this observation, we construct a model that represents hierarchical structures by introducing identifiers that encode parent–child relationships between joints within a sequence of joint positions. Experimental results on the MPII dataset demonstrate that the proposed method can successfully estimate joint types, hierarchical structures, and joint positions.
Keyword (in Japanese) (See Japanese page) 
(in English) Pose estimation / ResNet / Transformer / Sequence generation / Hierarchical structure / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 424, PRMU2025-54, pp. 68-72, March 2026.
Paper # PRMU2025-54 
Date of Issue 2026-03-17 (PRMU) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF PRMU2025-54

Conference Information
Committee PRMU IPSJ-CVIM IBISML ITE-SIP  
Conference Date 2026-03-24 - 2026-03-25 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2026-03-PRMU-CVIM-IBISML-SIP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Building a pose estimation model based on the hierarchical structure using the transformer decoder 
Sub Title (in English)  
Keyword(1) Pose estimation  
Keyword(2) ResNet  
Keyword(3) Transformer  
Keyword(4) Sequence generation  
Keyword(5) Hierarchical structure  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Kong Huijing  
1st Author's Affiliation Toyo University Graduate School (Toyo univ)
2nd Author's Name Makoto Murakami  
2nd Author's Affiliation Toyo University (Toyo univ)
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Speaker Author-1 
Date Time 2026-03-25 09:20:00 
Presentation Time 15 minutes 
Registration for PRMU 
Paper # PRMU2025-54 
Volume (vol) vol.125 
Number (no) no.424 
Page pp.68-72 
#Pages 5 
Date of Issue 2026-03-17 (PRMU) 

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