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

• URL: http://arxiv.org/abs/2307.06737v2
• Date: Sat, 20 Apr 2024 11:53:13 GMT
• Title: Improving 2D Human Pose Estimation in Rare Camera Views with Synthetic Data
• Authors: Miroslav Purkrabek, Jiri Matas,
• Abstract summary: We introduce RePoGen, 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.
• Score: 24.63316659365843
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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