MuTr: Multi-Stage Transformer for Hand Pose Estimation from Full-Scene Depth Image
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F23%3A43969579" target="_blank" >RIV/49777513:23520/23:43969579 - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/1424-8220/23/12/5509" target="_blank" >https://www.mdpi.com/1424-8220/23/12/5509</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/s23125509" target="_blank" >10.3390/s23125509</a>
Alternative languages
Result language
angličtina
Original language name
MuTr: Multi-Stage Transformer for Hand Pose Estimation from Full-Scene Depth Image
Original language description
This work presents a novel transformer-based method for hand pose estimation—DePOTR. We test the DePOTR method on four benchmark datasets, where DePOTR outperforms other transformer-based methods while achieving results on par with other state-of-the-art methods. To further demonstrate the strength of DePOTR, we propose a novel multi-stage approach from full-scene depth image—MuTr. MuTr removes the necessity of having two different models in the hand pose estimation pipeline—one for hand localization and one for pose estimation—while maintaining promising results. To the best of our knowledge, this is the first successful attempt to use the same model architecture in standard and simultaneously in full-scene image setup while achieving competitive results in both of them. On the NYU dataset, DePOTR and MuTr reach precision equal to 7.85 mm and 8.71 mm, respectively.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
<a href="/en/project/EF15_003%2F0000466" target="_blank" >EF15_003/0000466: Artificial Intelligence and Reasoning</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
SENSORS
ISSN
1424-8220
e-ISSN
1424-8220
Volume of the periodical
23
Issue of the periodical within the volume
12
Country of publishing house
CH - SWITZERLAND
Number of pages
16
Pages from-to
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UT code for WoS article
001017823100001
EID of the result in the Scopus database
2-s2.0-85163928315