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 However, older content developed for CRT or early Plasma screen TVs has become outdated quickly and no longer meets current aspect ratio and resolution standards.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.

翻译：现代显示设备的发明提升了各类内容的观看体验——从体育赛事到8K高清电影莫不如此。然而，为CRT或早期等离子电视开发的老旧内容已迅速过时，不再符合当前的宽高比与分辨率标准。本文探索是否可通过扩散模型解决此问题，使旧内容适配当代预期。我们研究了如何结合多个独立的计算机视觉任务，尝试解决老旧动画内容宽高比扩展的问题，使新内容对全新观众而言与原始素材难以区分。这些现有能力包括Stable Diffusion、内容感知场景检测、目标检测及关键点匹配。我们成功将这些任务串联起来生成合理输出，但未来仍需进一步改进并拓展至非动画内容领域。