  Unsupervised image-to-image translation methods have received a lot of
attention in the last few years. Multiple techniques emerged tackling the
initial challenge from different perspectives. Some focus on learning as much
as possible from several target style images for translations while other make
use of object detection in order to produce more realistic results on
content-rich scenes. In this work, we assess how a method that has initially
been developed for single object translation performs on more diverse and
content-rich images. Our work is based on the FUNIT[1] framework and we train
it with a more diverse dataset. This helps understanding how such method
behaves beyond their initial frame of application. We present a way to extend a
dataset based on object detection. Moreover, we propose a way to adapt the
FUNIT framework in order to leverage the power of object detection that one can
see in other methods.
