Stable Remaster: Bridging the Gap Between Old Content and New Displays
Nathan Paull Shuvam Keshari Yian Wong
The invention of modern displays has enhanced the viewer experience for any
kind of content: ranging from sports to movies in 8K high-definition
resolution. However, older content developed for CRT or early Plasma screen TVs
has become outdated quickly and no longer meets current aspect ratio and
resolution standards. In this paper, we explore whether we can solve this
problem with the use of diffusion models to adapt old content to meet
contemporary expectations. We explore the ability to combine multiple
independent computer vision tasks to attempt to solve the problem of expanding
aspect ratios of old animated content such that the new content would be
indistinguishable from the source material to a brand-new viewer. These
existing capabilities include Stable Diffusion, Content-Aware Scene Detection,
Object Detection, and Key Point Matching. We were able to successfully chain
these tasks together in a way that generated reasonable outputs, however,
future work needs to be done to improve and expand the application to
non-animated content as well.