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We propose a real time deep learning framework for video-based facialexpression capture. Our process uses a high-end facial capture pipeline basedon FACEGOOD to capture facial expression. We train a convolutional neuralnetwork to produce high-quality continuous blendshape weight output from videotraining. Since this facial capture is fully automated, our system candrastically reduce the amount of labor involved in the development of modernnarrative-driven video games or films involving realistic digital doubles ofactors and potentially hours of animated dialogue per character. We demonstratecompelling animation inference in challenging areas such as eyes and lips.

High-Quality Real Time Facial Capture Based on Single Camera