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Papers/Future Video Synthesis with Object Motion Prediction

Future Video Synthesis with Object Motion Prediction

Yue Wu, Rongrong Gao, Jaesik Park, Qifeng Chen

2020-04-01CVPR 2020 6Video Predictionmotion predictionPredictionPredict Future Video Frames
PaperPDFCode(official)

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.

Results

TaskDatasetMetricValueModel
VideoCityscapesLPIPS0.085FVS
VideoCityscapesMS-SSIM0.891FVS
VideoKITTILPIPS0.1848FVS
VideoKITTIMS-SSIM0.7928FVS
Video PredictionCityscapesLPIPS0.085FVS
Video PredictionCityscapesMS-SSIM0.891FVS
Video PredictionKITTILPIPS0.1848FVS
Video PredictionKITTIMS-SSIM0.7928FVS

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