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Future Video Synthesis with Object Motion Prediction

About

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.

Yue Wu, Rongrong Gao, Jaesik Park, Qifeng Chen• 2020

Related benchmarks

TaskDatasetResultRank
Future video predictionCityscapes Next 5 frames
MS-SSIM0.757
13
Future video predictionCityscapes Next 10 frames
LPIPS0.2328
13
Future video predictionCityscapes Next frame
MS-SSIM0.891
13
Future video predictionKITTI Next 3 frames
LPIPS0.246
11
Video PredictionCityscapes 9 (test)
MS-SSIM (t+1)89.1
11
Video PredictionCityscapes
MS-SSIM (t+1)89.1
11
Video PredictionKITTI 12 (test)
MS-SSIM (t+1)79.28
9
Video PredictionKITTI
MS-SSIM (t+1)79.28
9
Video PredictionCityscapes (test)
MS-SSIM (t+1)89.1
7
Future video predictionKITTI Next frame
MS-SSIM0.7928
6
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