Stable Remaster: Bridging the Gap Between Old Content and New Displays
This addresses the issue for media producers and consumers who want to remaster old animated content, though it is incremental as it builds on existing methods and has limited scope to non-animated content.
The paper tackles the problem of outdated aspect ratios and resolutions in older animated content by using diffusion models to adapt it for modern displays, achieving reasonable outputs through a chained approach of multiple computer vision tasks.
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 However, older content developed for CRT or early Plasma screen TVs has become outdated quickly and no longer meets current aspect ratio and resolution standards.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.