PeelNet: Textured 3D reconstruction of human body using single view RGB image

Reconstructing human shape and pose from a single image is a challenging problem due to issues like severe self-occlusions, clothing variations, and changes in lighting to name a few. Many applications in the entertainment industry, e-commerce, health-care (physiotherapy), and mobile-based AR/VR platforms can benefit from recovering the 3D human shape, pose, and texture. In this paper, we present PeelNet, an end-to-end generative adversarial framework to tackle the problem of textured 3D reconstruction of the human body from a single RGB image. Motivated by ray tracing for generating realistic images of a 3D scene, we tackle this problem by representing the human body as a set of peeled depth and RGB maps which are obtained by extending rays beyond the first intersection with the 3D object. This formulation allows us to handle self-occlusions efficiently. Current parametric model-based approaches fail to model loose clothing and surface-level details and are proposed for the underlying naked human body. Majority of non-parametric approaches are either computationally expensive or provide unsatisfactory results. We present a simple non-parametric solution where the peeled maps are generated from a single RGB image as input. Our proposed peeled depth maps are back-projected to 3D volume to obtain a complete 3D shape. The corresponding RGB maps provide vertex-level texture details. We compare our method against current state-of-the-art methods in 3D reconstruction and demonstrate the effectiveness of our method on BUFF and MonoPerfCap datasets.