Video Interpolation

Video interpolation aims to generate intermediate frames between existing ones in a video sequence, increasing the frame rate and improving visual smoothness. Current research focuses on leveraging deep learning models, including diffusion models, implicit neural representations, and transformers, often incorporating techniques like optical flow estimation and attention mechanisms to handle complex motion and occlusion. These advancements are improving the quality and efficiency of video interpolation, with applications ranging from enhancing video content for entertainment to improving the analysis of time-series data in scientific fields like meteorology and medical imaging.