  We propose a real time deep learning framework for video-based facial
expression capture. Our process uses a high-end facial capture pipeline based
on FACEGOOD to capture facial expression. We train a convolutional neural
network to produce high-quality continuous blendshape weight output from video
training. Since this facial capture is fully automated, our system can
drastically reduce the amount of labor involved in the development of modern
narrative-driven video games or films involving realistic digital doubles of
actors and potentially hours of animated dialogue per character. We demonstrate
compelling animation inference in challenging areas such as eyes and lips.
