Imitation Perspective Learning for Monocular RGB-D Human Pose and Shape Estimation

Abstract

In the realm of computer vision, the accurate estimation of human pose and shape from monocular RGB-D images stands as a critical challenge due to various factors such as occlusions, viewpoint variations, and depth ambiguities. To address these challenges, this paper introduces a novel framework termed Imitation Perspective Learning. Inspired by human cognition, which learns through observation and imitation, our approach leverages synthetic or simulated views to augment training data, providing the model with a diverse range of perspectives to learn from. By imitating these perspectives during training, our model enhances its ability to generalize across different scenarios and viewpoints, leading to more accurate and robust pose and shape estimates. We employ generative adversarial networks (GANs) to synthesize additional views from existing RGB-D data, effectively increasing the variability and richness of the training set. Experimental results on standard benchmark datasets demonstrate significant improvements in both accuracy and robustness compared to baseline methods, particularly in challenging scenarios such as occlusions and viewpoint variations. Our work sheds light on the potential of imitation perspective learning in advancing the field of monocular RGB-D human pose and shape estimation, with implications for applications in areas such as human-computer interaction, augmented reality, and healthcare. Extensive ablation studies analyze the effectiveness of different components within our framework, providing valuable insights into the contributions of imitation perspective learning. Overall, our approach represents a significant step forward in tackling the complexities of monocular RGB-D human pose and shape estimation, paving the way for more reliable and robust computer vision systems.