Dual Motion Attention and Enhanced Knowledge Distillation for Video Frame Interpolation

Video frame interpolation presents a formidable challenge in the domain of video generation, primarily due to the intricate motion dynamics exhibited by objects within video frames. With the advancements in deep learning, numerous flow-based methods for video frame interpolation have emerged. These methods aim to predict intermediate frames by leveraging the estimation of motion information between frames. In this paper, we propose a novel framework for modeling input video frames, which employs a coarse-to-fine structure to extract motion information between frames. Additionally, it incorporates a Bidirectional Correlation Volume and a complementary module of contextual features, specifically designed to pay attention to the symmetry of the optical flow and shallow motion features. We incorporate this dual attention to the knowledge distillation part of the model, which further improves the performance of the model. Leveraging this framework, our model demonstrates the ability to accurately predict motion information between frames, consequently producing visually appealing intermediate frames.The code is available at https://github.com/famt0531/DAEK.