Improving the Robustness of 3D Human Pose Estimation: A Benchmark and Learning from Noisy Input

• URL: http://arxiv.org/abs/2312.06797v2
• Date: Tue, 16 Apr 2024 03:10:34 GMT
• Title: Improving the Robustness of 3D Human Pose Estimation: A Benchmark and Learning from Noisy Input
• Authors: Trung-Hieu Hoang, Mona Zehni, Huy Phan, Duc Minh Vo, Minh N. Do,
• Abstract summary: In this work, we focus on the robustness of 2D-to-3D pose lifters. We observe the poor generalization of state-of-the-art 3D pose lifters in the presence of corruption. We introduce Temporal Additive Gaussian Noise (TAGN) as a simple yet effective 2D input pose data augmentation. To incorporate the confidence scores output by the 2D pose detectors, we design a confidence-aware convolution (CA-Conv) block.
• Score: 23.505846631252993
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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