This paper presents a new image-based virtual try-on approach (Outfit-VITON) that helps visualize how a composition of clothing items selected from various reference images form a cohesive outfit on a person in a query image. Our algorithm has two distinctive properties. First, it is inexpensive, as it simply requires a large set of single (non-corresponding) images (both real and catalog) of people wearing various garments without explicit 3D information. The training phase requires only single images, eliminating the need for manually creating image pairs, where one image shows a person wearing a particular garment and the other shows the same catalog garment alone. Secondly, it can synthesize images of multiple garments composed into a single, coherent outfit; and it enables control of the type of garments rendered in the final outfit. Once trained, our approach can then synthesize a cohesive outfit from multiple images of clothed human models, while fitting the outfit to the body shape and pose of the query person. An online optimization step takes care of fine details such as intricate textures and logos. Quantitative and qualitative evaluations on an image dataset containing large shape and style variations demonstrate superior accuracy compared to existing state-of-the-art methods, especially when dealing with highly detailed garments.

We propose a scheme for training a computerized agent to perform complex human tasks such as highway steering. The scheme is designed to follow a natural learning process whereby a human instructor teaches a computerized trainee. It enables leveraging the weak supervision abilities of a (human) instructor, who, while unable to perform well herself at the required task, can provide coherent and learnable instantaneous reward signals to the computerized trainee. The learning process consists of three supervised elements followed by reinforcement learning. The supervised learning stages are: (i) supervised imitation learning; (ii) supervised reward induction; and (iii) supervised safety module construction. We implemented this scheme using deep convolutional networks and applied it to successfully create a computerized agent capable of autonomous highway steering over the well-known racing game Assetto Corsa. We demonstrate that the use of all components is essential to effectively carry out reinforcement learning of the steering task using vision alone, without access to a driving simulator internals, and operating in wall-clock time.