Frame Synthesis

Frame synthesis focuses on generating realistic and temporally consistent video frames, either intermediate frames for interpolation/extrapolation or entirely new frames for video generation or action recognition. Current research emphasizes leveraging deep learning models, particularly diffusion models, generative adversarial networks (GANs), and transformers, often incorporating techniques like optical flow estimation, feature alignment (spatial and temporal), and recurrent networks to improve accuracy and efficiency. These advancements have significant implications for various applications, including video enhancement, animation, and video-based action recognition, by enabling higher-quality video processing and more efficient analysis.