A Novel All-In-One Grid Network For Video Frame Interpolation

Flow-based approaches for video frame interpolation typically consist of multiple networks that are responsible for feature extraction, optical flow estimation, and image synthesis, respectively. However, they are usually computationally expensive, and can hardly be employed in devices with limited computing resources. In this work, we propose an All-in-one Grid Frame Interpolation Network (AGFIN) to address this problem. AGFIN is a light-weight network with multiple rows and columns. In each row, we estimate the contextual features and optical flows, then the image synthesis module reconstructs the results from the warped frames and features. Each row serves as a coarser or finer auxiliary for the nearest row. In contrast to using multiple networks, our model integrates feature extraction, optical flow estimation, and image synthesis into a compact network. The experimental results show that our approach has better or comparable performance comparing to representative state-of-the-art approaches with less computational cost.