  In this paper, we develop a MultiTask Learning (MTL) model to achieve dense
predictions for comics panels to, in turn, facilitate the transfer of comics
from one publication channel to another by assisting authors in the task of
reconfiguring their narratives. Our MTL method can successfully identify the
semantic units as well as the embedded notion of 3D in comic panels. This is a
significantly challenging problem because comics comprise disparate artistic
styles, illustrations, layouts, and object scales that depend on the authors
creative process. Typically, dense image-based prediction techniques require a
large corpus of data. Finding an automated solution for dense prediction in the
comics domain, therefore, becomes more difficult with the lack of ground-truth
dense annotations for the comics images. To address these challenges, we
develop the following solutions: 1) we leverage a commonly-used strategy known
as unsupervised image-to-image translation, which allows us to utilize a large
corpus of real-world annotations; 2) we utilize the results of the translations
to develop our multitasking approach that is based on a vision transformer
backbone and a domain transferable attention module; 3) we study the
feasibility of integrating our MTL dense-prediction method with an existing
retargeting method, thereby reconfiguring comics.
