  Since the generative neural networks have made a breakthrough in the image
generation problem, lots of researches on their applications have been studied
such as image restoration, style transfer and image completion. However, there
has been few research generating objects in uncontrolled real-world
environments. In this paper, we propose a novel approach for vehicle image
generation in real-world scenes. Using a subnetwork based on a precedent work
of image completion, our model makes the shape of an object. Details of objects
are trained by an additional colorization and refinement subnetwork, resulting
in a better quality of generated objects. Unlike many other works, our method
does not require any segmentation layout but still makes a plausible vehicle in
the image. We evaluate our method by using images from Berkeley Deep Drive
(BDD) and Cityscape 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 and the FID score.
