PoseBench: Benchmarking the Robustness of Pose Estimation Models under Corruptions
<|reference_start|>PoseBench: Benchmarking the Robustness of Pose Estimation Models under Corruptions: Pose estimation aims to accurately identify anatomical keypoints in humans and animals using monocular images, which is crucial for various applications such as human-machine interaction, embodied AI, and autonomous driving. While current models show promising results, they are typically trained and tested on clean data, potentially overlooking the corruption during real-world deployment and thus posing safety risks in practical scenarios. To address this issue, we introduce PoseBench, a comprehensive benchmark designed to evaluate the robustness of pose estimation models against real-world corruption. We evaluated 60 representative models, including top-down, bottom-up, heatmap-based, regression-based, and classification-based methods, across three datasets for human and animal pose estimation. Our evaluation involves 10 types of corruption in four categories: 1) blur and noise, 2) compression and color loss, 3) severe lighting, and 4) masks. Our findings reveal that state-of-the-art models are vulnerable to common real-world corruptions and exhibit distinct behaviors when tackling human and animal pose estimation tasks. To improve model robustness, we delve into various design considerations, including input resolution, pre-training datasets, backbone capacity, post-processing, and data augmentations. We hope that our benchmark will serve as a foundation for advancing research in robust pose estimation. The benchmark and source code will be released at https://xymsh.github.io/PoseBench<|reference_end|>
@article{ma2024posebench:,
    title={PoseBench: Benchmarking the Robustness of Pose Estimation Models under
  Corruptions},
    author={Sihan Ma, Jing Zhang, Qiong Cao, Dacheng Tao},
    journal={arXiv preprint arXiv:2406.14367},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.14367},
    primaryClass={cs.CV cs.AI}
}