Given a pair of images---target person and garment on another person---we automatically generate the target person in the given garment. Previous methods mostly focused on texture transfer via paired data training, while overlooking body shape deformations, skin color, and seamless blending of garment with the person. This work focuses on those three components, while also not requiring paired data training. We designed a pose conditioned StyleGAN2 architecture with a clothing segmentation branch that is trained on images of people wearing garments. Once trained, we propose a new layered latent space interpolation method that allows us to preserve and synthesize skin color and target body shape while transferring the garment from a different person. We demonstrate results on high resolution 512 × 512 images, and extensively compare to state of the art in try-on on both latent space generated and real images.

Road surface inspection in cities is for the most part, a task performed manually. Being a subjective and labor intensive process, it is an ideal candidate for automation. We propose a solution based on computer vision and data-driven methods to detect distress on the road surface. Our method works on images collected from a camera mounted on the windshield of a vehicle. We use an automatic procedure to select images suitable for inspection based on lighting and weather conditions. From the selected data we segment the ground plane and use texture, color and location information to detect the presence of pavement distress. We describe an over-segmentation algorithm that identifies coherent image regions not just in terms of color, but also texture. We also discuss the problem of learning from unreliable human-annotations and propose using a weakly supervised learning algorithm (Multiple Instance Learning) to train a classifier. We present results from experiments comparing the performance of this approach against multiple individual human labelers, with the ground-truth labels obtained from an ensemble of other human labelers. Finally, we show results of pavement distress scores computed using our method over a subset of a citywide road network.