14/06/2020

Future Video Synthesis With Object Motion Prediction

Yue Wu, Rongrong Gao, Jaesik Park, Qifeng Chen

Keywords: future video prediction, spatial transformation, generative adversarial networks

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.

 0
 0
 0
 0
This is an embedded video. Talk and the respective paper are published at CVPR 2020 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd

Similar Papers