Improving 2D Human Pose Estimation in Rare Camera Views with Synthetic Data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00376983" target="_blank" >RIV/68407700:21230/24:00376983 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/FG59268.2024.10582011" target="_blank" >https://doi.org/10.1109/FG59268.2024.10582011</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/FG59268.2024.10582011" target="_blank" >10.1109/FG59268.2024.10582011</a>
Alternative languages
Result language
angličtina
Original language name
Improving 2D Human Pose Estimation in Rare Camera Views with Synthetic Data
Original language description
Methods and datasets for human pose estimation focus predominantly on side- and front-view scenarios. We overcome the limitation by leveraging synthetic data and introduce RePoGen (RarE POses GENerator), an SMPL-based method for generating synthetic humans with comprehensive control over pose and view. Experiments on top-view datasets and a new dataset of real images with diverse poses show that adding the RePoGen data to the COCO dataset outperforms previous approaches to top- and bottom-view pose estimation without harming performance on common views. An ablation study shows that anatomical plausibility, a property prior research focused on, is not a prerequisite for effective performance. The introduced dataset and the corresponding code are available on the project website.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG)
ISBN
979-8-3503-9495-5
ISSN
2326-5396
e-ISSN
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Number of pages
9
Pages from-to
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Publisher name
IEEE Computer Society Press
Place of publication
New York
Event location
Istanbul
Event date
May 27, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001270976600125