Estimating Human Shape Under Clothing from Single Frontal View Point Cloud of a Dressed Human

Estimating human shape under clothing is a challenging task. We propose the first method to estimate accurate shape parameters from single-frame frontal view point cloud. To account for casual clothing, we personalize the original SMPL model to describe clothing as deviation from naked human parametric model, define a novel method to search for corresponding vertex pairs, and design a novel objective function that enforces point cloud vertices to remain outside of the naked body shape and tightly cling the personalized shape. Consolidating these three parts, our method integrates the advantages of free deformation method and model-based method. Our method is more effective than previous works in dealing with casual clothing situation. We evaluate the accuracy of estimated shape on noisy point cloud data captured by a commodity depth sensor.