  We address the task of multi-view image-to-image translation for person image
generation. The goal is to synthesize photo-realistic multi-view images with
pose-consistency across all views. Our proposed end-to-end framework is based
on a joint learning of multiple unpaired image-to-image translation models, one
per camera viewpoint. The joint learning is imposed by constraints on the
shared 3D human pose in order to encourage the 2D pose projections in all views
to be consistent. Experimental results on the CMU-Panoptic dataset demonstrate
the effectiveness of the suggested framework in generating photo-realistic
images of persons with new poses that are more consistent across all views in
comparison to a standard Image-to-Image baseline. The code is available at:
https://github.com/sony-si/MultiView-Img2Img
