Camera Distortion-aware 3D Human Pose Estimation in Video with Optimization-based Meta-Learning

• URL: http://arxiv.org/abs/2111.15056v1
• Date: Tue, 30 Nov 2021 01:35:04 GMT
• Title: Camera Distortion-aware 3D Human Pose Estimation in Video with Optimization-based Meta-Learning
• Authors: Hanbyel Cho, Yooshin Cho, Jaemyung Yu, Junmo Kim
• Abstract summary: Existing 3D human pose estimation algorithms trained on distortion-free datasets suffer performance drop when applied to new scenarios with a specific camera distortion. We propose a simple yet effective model for 3D human pose estimation in video that can quickly adapt to any distortion environment.
• Score: 23.200130129530653
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing 3D human pose estimation algorithms trained on distortion-free datasets suffer performance drop when applied to new scenarios with a specific camera distortion. In this paper, we propose a simple yet effective model for 3D human pose estimation in video that can quickly adapt to any distortion environment by utilizing MAML, a representative optimization-based meta-learning algorithm. We consider a sequence of 2D keypoints in a particular distortion as a single task of MAML. However, due to the absence of a large-scale dataset in a distorted environment, we propose an efficient method to generate synthetic distorted data from undistorted 2D keypoints. For the evaluation, we assume two practical testing situations depending on whether a motion capture sensor is available or not. In particular, we propose Inference Stage Optimization using bone-length symmetry and consistency. Extensive evaluation shows that our proposed method successfully adapts to various degrees of distortion in the testing phase and outperforms the existing state-of-the-art approaches. The proposed method is useful in practice because it does not require camera calibration and additional computations in a testing set-up.

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