Future Video Synthesis with Object Motion Prediction

TL;DR

This paper introduces a novel method for future video frame prediction that models scene dynamics by separately predicting background deformation and object motion, resulting in higher quality synthesized videos.

Contribution

The approach uniquely decouples background and object motion prediction, improving visual quality and reducing artifacts in future video synthesis.

Findings

Outperforms state-of-the-art methods on Cityscapes and KITTI datasets.

Produces videos with fewer tearing and distortion artifacts.

Achieves higher accuracy in scene dynamics modeling.

Abstract

We present an approach to predict future video frames given a sequence of continuous video frames in the past. Instead of synthesizing images directly, our approach is designed to understand the complex scene dynamics by decoupling the background scene and moving objects. The appearance of the scene components in the future is predicted by non-rigid deformation of the background and affine transformation of moving objects. The anticipated appearances are combined to create a reasonable video in the future. With this procedure, our method exhibits much less tearing or distortion artifact compared to other approaches. Experimental results on the Cityscapes and KITTI datasets show that our model outperforms the state-of-the-art in terms of visual quality and accuracy.