  Recent image-to-image translation models have shown great success in mapping
local textures between two domains. Existing approaches rely on a
cycle-consistency constraint that supervises the generators to learn an inverse
mapping. However, learning the inverse mapping introduces extra trainable
parameters and it is unable to learn the inverse mapping for some domains. As a
result, they are ineffective in the scenarios where (i) multiple visual image
domains are involved; (ii) both structure and texture transformations are
required; and (iii) semantic consistency is preserved. To solve these
challenges, the paper proposes a unified model to translate images across
multiple domains with significant domain gaps. Unlike previous models that
constrain the generators with the ubiquitous cycle-consistency constraint to
achieve the content similarity, the proposed model employs a perceptual
self-regularization constraint. With a single unified generator, the model can
maintain consistency over the global shapes as well as the local texture
information across multiple domains. Extensive qualitative and quantitative
evaluations demonstrate the effectiveness and superior performance over
state-of-the-art models. It is more effective in representing shape deformation
in challenging mappings with significant dataset variation across multiple
domains.
