2D Pose-guided Complete Silhouette Estimation of Human Body in Occlusion

Song Xu, Xuefeng Li, Xiangbo Lin

Abstract: Estimating complete human and hand silhouettes from a monocular RGB image has important roles in many visual applications such as image editing and action recognition. However, complicated scenes, non-rigid articulated deformations and occlusions make it a difficult task. To overcome the difficulties of inferring the invisible part of the human body due to objects’ occlusion, we propose a pose guided two stage deep neural network model. It leverages the key points semantic prior knowledge to help the model envision occluded areas, which is more in line with human’s visual recognition characteristics. The proposed model is validated on both hand and human datasets with varied occlusions. The experimental results are satisfying and better than the state-of-the-art methods.