  Recently, image-to-image translation has made significant progress in
achieving both multi-label (\ie, translation conditioned on different labels)
and multi-style (\ie, generation with diverse styles) tasks. However, due to
the unexplored independence and exclusiveness in the labels, existing endeavors
are defeated by involving uncontrolled manipulations to the translation
results. In this paper, we propose Hierarchical Style Disentanglement (HiSD) to
address this issue. Specifically, we organize the labels into a hierarchical
tree structure, in which independent tags, exclusive attributes, and
disentangled styles are allocated from top to bottom. Correspondingly, a new
translation process is designed to adapt the above structure, in which the
styles are identified for controllable translations. Both qualitative and
quantitative results on the CelebA-HQ dataset verify the ability of the
proposed HiSD. We hope our method will serve as a solid baseline and provide
fresh insights with the hierarchically organized annotations for future
research in image-to-image translation. The code has been released at
https://github.com/imlixinyang/HiSD.
