Intra- and Inter-frame Iterative Temporal Convolutional Networks for Video Stabilization

Video jitter is an uncomfortable product of irregular lens motion in time sequence. How to extract motion state information in a period of continuous video frames is a major issue for video stabilization. In this paper, we propose a novel sequence model, Intra- and Inter-frame Iterative Temporal Convolutional Networks (I3TC-Net), which alternatively transfer the spatial-temporal correlation of motion within and between frames. We hypothesize that the motion state information can be represented by transmission states. Specifically, we employ combination of Convolutional Long Short-Term Memory (ConvLSTM) and embedded encoder-decoder to generate the latent stable frame, which are used to update transmission states iteratively and learn a global homography transformation effectively for each unstable frame to generate the corresponding stabilized result along the time axis. Furthermore, we create a video dataset to solve the lack of stable data and improve the training effect. Experimental results show that our method outperforms state-of-the-art results on publicly available videos, such as 5.4 points improvements in stability score. The project page is available at https://github.com/root2022IIITC/IIITC.