2020 · 92 citations · 48 references
Scene AnalysisEngineeringVideo ProcessingVisual QualityImage AnalysisPattern RecognitionMotion PredictionRobot LearningVideo RestorationMachine VisionVideo ManipulationFuture Video SynthesisVideo UnderstandingFuture Video FramesDeep LearningComputer VisionContinuous Video FramesExtended RealityVideo Hallucination
The study proposes a method to predict future video frames from past continuous video sequences. The method models scene dynamics by separating background and moving objects, predicting future appearances through non‑rigid background deformation and affine object transformation, then fusing them to synthesize future frames. The approach achieves superior visual quality and accuracy, reducing tearing and distortion artifacts, and outperforms state‑of‑the‑art methods on Cityscapes and KITTI.
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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Deep Residual Learning for Image Recognition
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