We present a technique for learning the artistic style of a painting and applying it to a real-time video feed. The video feed is stylized with a convolutional neural network so that it gets the style of the original painting.
Our improvement over previous style-transfer works are two-fold:
- Stablizing the stylization so that the stylized video has temporal coherence. This is done with regularization during training. It prevents style features from jumping around from frame to frame.
- Using a pipeline for performing the stylization in real-time, with low-latency, by keeping the data on the GPU from end-to-end.
Jeffrey Rainy is an Applied Research Scientist at Element AI. He is interested in applying Artifical Intelligence and Machine Learning to real-world challenges. Background in software development, games, machine learning, fintech and self-driving vehicles.