dyelax/Adversarial_Video_Generation
A TensorFlow Implementation of "Deep Multi-Scale Video Prediction Beyond Mean Square Error" by Mathieu, Couprie & LeCun.
This project helps video game researchers and AI developers create clearer, more stable predictions of future video frames. Given a sequence of past video frames (like from Ms. Pac-Man gameplay), it generates the next frame in the sequence, producing sharper and more consistent visuals compared to traditional methods. Researchers studying generative models and video prediction will find this useful for evaluating adversarial techniques.
746 stars. No commits in the last 6 months.
Use this if you need to generate realistic, sharp future frames from a video sequence, especially for tasks where maintaining visual fidelity over time is crucial.
Not ideal if your primary goal is perfect, pixel-by-pixel accuracy with ground truth, as this focuses on visual plausibility and sharpness rather than exact matches.
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746
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185
Language
Python
License
MIT
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Last pushed
Oct 23, 2021
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