Texture improvement for human shape estimation from a single image

Current human digitization techniques from a single image are showing promising results when it comes to the quality of the estimated geometry, but they often fall short when it comes to the texture of the generated 3D model, especially on the occluded side of the person, while some others do not even output a texture for the model. Our goal in this paper is to improve the predicted texture of these models without requiring any other additional input more than the original image used to generate the 3D model in the first place. For that, we propose a novel way to predict the back view of the person by including semantic and positional information that outperforms the state-of-the-art techniques. Our method is based on a general-purpose image-to-image translation algorithm with conditional adversarial networks adapted to predict the back view of a human. Furthermore, we use the predicted image to improve the texture of the 3D estimated model and we provide a 3D dataset, V-Human, to train our method and also any 3D human shape estimation algorithms which use meshes such as PIFu.