Papers › Future Video Synthesis with Object Motion Prediction

Future Video Synthesis with Object Motion Prediction

1 Apr 2020CVPR 2020 6arXiv:2004.00542archive 2025-07-28

Yue Wu, Rongrong Gao, Jaesik Park, Qifeng Chen

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.

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ObjectPredict Future Video FramesPredictionVideo Predictionmotion prediction

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Video Prediction Cityscapes FVS LPIPS 0.0850 #2 of 3 Archive leaderboard report
Video Prediction Cityscapes FVS MS-SSIM 0.8910 #2 of 3 Archive leaderboard report
Video Prediction KITTI FVS LPIPS 0.1848 #2 of 3 Archive leaderboard report
Video Prediction KITTI FVS MS-SSIM 0.7928 #2 of 3 Archive leaderboard report

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