  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.
