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arXiv · 2307.06737

Improving 2D Human Pose Estimation in Rare Camera Views with Synthetic Data

Miroslav Purkrabek·Jiri Matas
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Abstract

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 https://mirapurkrabek.github.io/RePoGen-paper/ .

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Miroslav Purkrabek, Jiri Matas. 2023-07-13. Improving 2D Human Pose Estimation in Rare Camera Views with Synthetic Data. https://doi.org/10.1109/fg59268.2024.10582011

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