A Knowledge Distillation Architecture for Pressure Based In-Bed Human Body Reconstruction

This paper addresses the critical challenge of supine human mesh reconstruction in clinical monitoring scenarios through an innovative knowledge distillation framework. Confronting the inherent limitations of pressure sensor data—including limb occlusion artifacts and limited 3D expressiveness—we propose a hierarchical teacher-student architecture that synergistically integrates cross-modal knowledge from visual domain expertise. Our method leverages a pre-trained CLIFF model as the teacher to guide pressure-map student networks ResNet variants in estimating SMPL body parameters. The framework achieved 2-4$ $ error reduction across key metrics. This work propose a new solution to optimize pressure-based human body reconstruction and multimodal datasets utilization.