  Much of the state-of-the-art in image synthesis inspired by real artwork are
either entirely generative by filtered random noise or inspired by the transfer
of style. This work explores the application of image inpainting to continue
famous artworks and produce generative art with a Conditional GAN. During the
training stage of the process, the borders of images are cropped, leaving only
the centre. An inpainting GAN is then tasked with learning to reconstruct the
original image from the centre crop by way of minimising both adversarial and
absolute difference losses, which are analysed by both their Fr\'echet
Inception Distances and manual observations which are presented. Once the
network is trained, images are then resized rather than cropped and presented
as input to the generator. Following the learning process, the generator then
creates new images by continuing from the edges of the original piece. Three
experiments are performed with datasets of 4766 landscape paintings
(impressionism and romanticism), 1167 Ukiyo-e works from the Japanese Edo
period, and 4968 abstract artworks. Results show that geometry and texture
(including canvas and paint) as well as scenery such as sky, clouds, water,
land (including hills and mountains), grass, and flowers are implemented by the
generator when extending real artworks. In the Ukiyo-e experiments, it was
observed that features such as written text were generated even in cases where
the original image did not have any, due to the presence of an unpainted border
within the input image.
