Improving 2D Human Pose Estimation in Rare Camera Views with Synthetic Data
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arXiv
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909254580961280 |
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| author | Purkrabek, Miroslav Matas, Jiri |
| author_facet | Purkrabek, Miroslav Matas, Jiri |
| contents | 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/ . |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2307_06737 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Improving 2D Human Pose Estimation in Rare Camera Views with Synthetic Data Purkrabek, Miroslav Matas, Jiri Computer Vision and Pattern Recognition 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/ . |
| title | Improving 2D Human Pose Estimation in Rare Camera Views with Synthetic Data |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2307.06737 |