Interpolation Transformer

Interpolation transformers are neural network architectures designed to enhance image and video interpolation tasks, aiming to generate high-quality intermediate frames between existing ones. Current research focuses on improving efficiency and accuracy, particularly for challenging scenarios like large motions and blurry input, employing model architectures that combine convolutional layers with transformer blocks to leverage both local and long-range dependencies. These advancements have significant implications for video processing applications, including video coding, broadcasting, and the creation of high-frame-rate video from lower-frame-rate sources.