Generating objects going well with the surroundings

Since the generative adversarial network has made a breakthrough in the image generation problem, lots of researches on its applications have been studied such as image restoration, style transfer and image completion. However, there have been few researches generating objects in uncontrolled real-world environments. In this paper, we propose a novel approach for image generation in real-world scenes. The overall architecture consists of two different networks each of which completes the shape of the generating object and paints the context on it respectively. Using a subnetwork proposed in a precedent work of image completion, our model make the shape of an object. Unlike the approaches used in the image completion problem, details of trained objects are encoded into a latent variable by an additional subnetwork, resulting in a better quality of the generated objects. We evaluated our method using KITTI and City-scape datasets, which are widely used for object detection and image segmentation problems. The adequacy of the generated images by the proposed method has also been evaluated using a widely utilized object detection algorithm.