Video Prediction
125 papers with code • 13 benchmarks • 17 datasets
Video Prediction is the task of predicting future frames given past video frames.
Source: Photo-Realistic Video Prediction on Natural Videos of Largely Changing Frames
Libraries
Use these libraries to find Video Prediction models and implementationsDatasets
Most implemented papers
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
Here, we explore prediction of future frames in a video sequence as an unsupervised learning rule for learning about the structure of the visual world.
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time.
Video-to-Video Synthesis
We study the problem of video-to-video synthesis, whose goal is to learn a mapping function from an input source video (e. g., a sequence of semantic segmentation masks) to an output photorealistic video that precisely depicts the content of the source video.
Deep multi-scale video prediction beyond mean square error
Learning to predict future images from a video sequence involves the construction of an internal representation that models the image evolution accurately, and therefore, to some degree, its content and dynamics.
The "something something" video database for learning and evaluating visual common sense
Neural networks trained on datasets such as ImageNet have led to major advances in visual object classification.
PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning
We present PredRNN++, an improved recurrent network for video predictive learning.
Learning a Driving Simulator
Comma. ai's approach to Artificial Intelligence for self-driving cars is based on an agent that learns to clone driver behaviors and plans maneuvers by simulating future events in the road.
Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model
To address these problems, we propose both a new model and a benchmark for precipitation nowcasting.
Prediction Under Uncertainty with Error-Encoding Networks
In this work we introduce a new framework for performing temporal predictions in the presence of uncertainty.
Stochastic Video Generation with a Learned Prior
Sample generations are both varied and sharp, even many frames into the future, and compare favorably to those from existing approaches.