  We propose a novel spatially-correlative loss that is simple, efficient and
yet effective for preserving scene structure consistency while supporting large
appearance changes during unpaired image-to-image (I2I) translation. Previous
methods attempt this by using pixel-level cycle-consistency or feature-level
matching losses, but the domain-specific nature of these losses hinder
translation across large domain gaps. To address this, we exploit the spatial
patterns of self-similarity as a means of defining scene structure. Our
spatially-correlative loss is geared towards only capturing spatial
relationships within an image rather than domain appearance. We also introduce
a new self-supervised learning method to explicitly learn spatially-correlative
maps for each specific translation task. We show distinct improvement over
baseline models in all three modes of unpaired I2I translation: single-modal,
multi-modal, and even single-image translation. This new loss can easily be
integrated into existing network architectures and thus allows wide
applicability.
