Domain-Generalized 3D Human Pose Estimation: The Dual-Augmentor Framework

Abstract

This paper presents the Dual-Augmentor Framework for achieving domain-generalized 3D human pose estimation. Domain generalization is a critical challenge in pose estimation due to variations in environmental conditions, camera viewpoints, and human activities across different domains. The proposed framework integrates two distinct augmentors: one focusing on domain-specific features and the other on domain-agnostic representations. Through this approach, the framework aims to bridge the domain gap and enhance the model's adaptability to diverse environments. Experimental evaluation demonstrates the effectiveness of the Dual-Augmentor Framework in achieving superior performance in accuracy, robustness, and generalization across diverse domains compared to existing methods. The insights provided into the contributions of each augmentor further enhance our understanding of the framework's efficacy. Overall, the Dual-Augmentor Framework represents a significant advancement in addressing the challenges of domain-generalized 3D human pose estimation. By integrating two augmentors, one focusing on domain-specific features and the other on domain-agnostic representations, our approach aims to bridge this gap effectively. Experimental results showcase the framework's superiority in accuracy, robustness, and generalization across diverse domains compared to existing methods. Insights into the individual contributions of each augmentor further elucidate the framework's effectiveness. Overall, the Dual-Augmentor Framework represents a significant advancement in domain-generalized 3D human pose estimation, with broad implications for applications in computer vision and beyond.