Achieving Domain Generalization in 3D Human Pose Estimation via a Dual-Augmentor Approach

Abstract

This paper presents a novel approach to achieving domain generalization in 3D human pose estimation through a Dual-Augmentor framework. Domain shift poses a significant challenge in deploying pose estimation models across diverse environments, where models trained on data from one domain often fail to generalize well to unseen domains. To address this challenge, we propose a Dual-Augmentor approach that integrates two distinct augmentors: one focusing on domain-specific features and the other on domain-agnostic representations. By leveraging this dual augmentation strategy, our framework aims to bridge the domain gap and enhance the model's adaptability to diverse environments. Extensive experimentation and evaluation demonstrate the effectiveness of the Dual-Augmentor approach, outperforming existing methods in terms of accuracy, robustness, and generalization across diverse domains. Overall, our approach offers promising opportunities for advancing the capabilities of 3D human pose estimation systems in real-world applications. Extensive experimentation and evaluation on benchmark datasets demonstrate the efficacy of the Dual-Augmentor Approach, showcasing superior performance in terms of accuracy, robustness, and generalization across diverse domains compared to existing methods. Insights into the contributions of each augmentor and their combined impact on improving pose estimation accuracy are also provided. Overall, the Dual-Augmentor Approach represents a significant advancement in addressing the challenges of domain generalization in 3D human pose estimation, with promising implications for real-world applications in computer vision and beyond.