  Accurately labeled real-world training data can be scarce, and hence recent
works adapt, modify or generate images to boost target datasets. However,
retaining relevant details from input data in the generated images is
challenging and failure could be critical to the performance on the final task.
In this work, we synthesize person-specific eye images that satisfy a given
semantic segmentation mask (content), while following the style of a specified
person from only a few reference images. We introduce two approaches, (a) one
used to win the OpenEDS Synthetic Eye Generation Challenge at ICCV 2019, and
(b) a principled approach to solving the problem involving simultaneous
injection of style and content information at multiple scales. Our
implementation is available at https://github.com/mcbuehler/Seg2Eye.
