  Image-to-image translation is significant to many computer vision and machine
learning tasks such as image synthesis and video synthesis. It has primary
applications in the graphics editing and animation industries. With the
development of generative adversarial networks, a lot of attention has been
drawn to image-to-image translation tasks. In this paper, we propose and
investigate a novel task named as panoptic-level image-to-image translation and
a naive baseline of solving this task. Panoptic-level image translation extends
the current image translation task to two separate objectives of semantic style
translation (adjust the style of objects to that of different domains) and
instance transfiguration (swap between different types of objects). The
proposed task generates an image from a complete and detailed panoptic
perspective which can enrich the context of real-world vision synthesis. Our
contribution consists of the proposal of a significant task worth investigating
and a naive baseline of solving it. The proposed baseline consists of the
multiple instances sequential translation and semantic-level translation with
domain-invariant content code.
