Improving the Robustness of 3D Human Pose Estimation: A Benchmark and Learning from Noisy Input
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866929314819211264 |
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| author | Hoang, Trung-Hieu Zehni, Mona Phan, Huy Vo, Duc Minh Do, Minh N. |
| author_facet | Hoang, Trung-Hieu Zehni, Mona Phan, Huy Vo, Duc Minh Do, Minh N. |
| contents | Despite the promising performance of current 3D human pose estimation techniques, understanding and enhancing their generalization on challenging in-the-wild videos remain an open problem. In this work, we focus on the robustness of 2D-to-3D pose lifters. To this end, we develop two benchmark datasets, namely Human3.6M-C and HumanEva-I-C, to examine the robustness of video-based 3D pose lifters to a wide range of common video corruptions including temporary occlusion, motion blur, and pixel-level noise. We observe the poor generalization of state-of-the-art 3D pose lifters in the presence of corruption and establish two techniques to tackle this issue. First, we introduce Temporal Additive Gaussian Noise (TAGN) as a simple yet effective 2D input pose data augmentation. Additionally, to incorporate the confidence scores output by the 2D pose detectors, we design a confidence-aware convolution (CA-Conv) block. Extensively tested on corrupted videos, the proposed strategies consistently boost the robustness of 3D pose lifters and serve as new baselines for future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_06797 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Improving the Robustness of 3D Human Pose Estimation: A Benchmark and Learning from Noisy Input Hoang, Trung-Hieu Zehni, Mona Phan, Huy Vo, Duc Minh Do, Minh N. Computer Vision and Pattern Recognition Despite the promising performance of current 3D human pose estimation techniques, understanding and enhancing their generalization on challenging in-the-wild videos remain an open problem. In this work, we focus on the robustness of 2D-to-3D pose lifters. To this end, we develop two benchmark datasets, namely Human3.6M-C and HumanEva-I-C, to examine the robustness of video-based 3D pose lifters to a wide range of common video corruptions including temporary occlusion, motion blur, and pixel-level noise. We observe the poor generalization of state-of-the-art 3D pose lifters in the presence of corruption and establish two techniques to tackle this issue. First, we introduce Temporal Additive Gaussian Noise (TAGN) as a simple yet effective 2D input pose data augmentation. Additionally, to incorporate the confidence scores output by the 2D pose detectors, we design a confidence-aware convolution (CA-Conv) block. Extensively tested on corrupted videos, the proposed strategies consistently boost the robustness of 3D pose lifters and serve as new baselines for future research. |
| title | Improving the Robustness of 3D Human Pose Estimation: A Benchmark and Learning from Noisy Input |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2312.06797 |