  In the field of Image-to-Image (I2I) translation, ensuring consistency
between input images and their translated results is a key requirement for
producing high-quality and desirable outputs. Previous I2I methods have relied
on result consistency, which enforces consistency between the translated
results and the ground truth output, to achieve this goal. However, result
consistency is limited in its ability to handle complex and unseen attribute
changes in translation tasks. To address this issue, we introduce a
transition-aware approach to I2I translation, where the data translation
mapping is explicitly parameterized with a transition variable, allowing for
the modelling of unobserved translations triggered by unseen transitions.
Furthermore, we propose the use of transition consistency, defined on the
transition variable, to enable regularization of consistency on unobserved
translations, which is omitted in previous works. Based on these insights, we
present Unseen Transition Suss GAN (UTSGAN), a generative framework that
constructs a manifold for the transition with a stochastic transition encoder
and coherently regularizes and generalizes result consistency and transition
consistency on both training and unobserved translations with tailor-designed
constraints. Extensive experiments on four different I2I tasks performed on
five different datasets demonstrate the efficacy of our proposed UTSGAN in
performing consistent translations.
