  Video holds significance in computer graphics applications. Because of the
heterogeneous of digital devices, retargeting videos becomes an essential
function to enhance user viewing experience in such applications. In the
research of video retargeting, preserving the relevant visual content in
videos, avoiding flicking, and processing time are the vital challenges.
Extending image retargeting techniques to the video domain is challenging due
to the high running time. Prior work of video retargeting mainly utilizes
time-consuming preprocessing to analyze frames. Plus, being tolerant of
different video content, avoiding important objects from shrinking, and the
ability to play with arbitrary ratios are the limitations that need to be
resolved in these systems requiring investigation. In this paper, we present an
end-to-end RETVI method to retarget videos to arbitrary aspect ratios. We
eliminate the computational bottleneck in the conventional approaches by
designing RETVI with two modules, content feature analyzer (CFA) and adaptive
deforming estimator (ADE). The extensive experiments and evaluations show that
our system outperforms previous work in quality and running time. Visit our
project website for more results at http://graphics.csie.ncku.edu.tw/RETVI.
