3260 papers • 126 benchmarks • 313 datasets
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These leaderboards are used to track progress in video-prediction
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Use these libraries to find video-prediction models and implementations
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The Decompositional Disentangled Predictive Auto-Encoder (DDPAE) is proposed, a framework that combines structured probabilistic models and deep networks to automatically decompose the high-dimensional video that the authors aim to predict into components, and disentangle each component to have low-dimensional temporal dynamics that are easier to predict.
An approach to predict future video frames given a sequence of continuous video frames in the past by decoupling the background scene and moving objects and shows that this model outperforms the state-of-the-art in terms of visual quality and accuracy.
A computational model for high-fidelity video prediction which disentangles motion-specific propagation from motion-agnostic generation and introduces a confidence-aware warping operator which gates the output of pixel predictions from a flow predictor for non-occluded regions and from a context encoder for occluded regions.
This work is the first to propose multi-frame video prediction with normalizing flows, which allows for direct optimization of the data likelihood, and produces high-quality stochastic predictions.
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