Artificial View Representation Learning for Monocular RGB-D Human Pose and Shape Estimation

Abstract

In the domain of computer vision, the precise estimation of human pose and shape from monocular RGB-D images is essential for various applications, including augmented reality, biomechanics, and human-computer interaction. Traditional approaches often struggle to generalize effectively across different viewpoints and environmental conditions, limiting their accuracy and robustness. In response, this paper introduces a novel paradigm termed Artificial View Representation Learning aimed at enhancing pose and shape estimation through the synthesis of artificial perspectives. By training on a combination of real and synthetic views, Artificial View Representation Learning leverages the strengths of both modalities to improve generalization across diverse scenarios. Empirical evaluations demonstrate significant enhancements in accuracy, robustness, and generalization ability compared to traditional methods, particularly in challenging conditions such as occlusions and cluttered environments. This paper presents a comprehensive overview of Artificial View Representation Learning, including its principles, methodologies, experimental validations, and potential applications. Ultimately, this paradigm represents a significant step forward in the field of monocular RGB-D human pose and shape estimation, with implications for a wide range of domains requiring precise understanding of human movements.