. A machine learning approach is used to learn a regression function mapping phoneme labels to speech animation. Our approach generates continuous, natural-looking speech animation for a reference face parameterization that can be retargeted to the face of any computer generated character. We introduce a simple and efective deep learning approach to automatically generate natural looking speech animation that synchronizes to input speech. Our approach uses a sliding window predictor that learns arbitrary nonlinear mappings from phoneme label input sequences to mouth movements in a way that accurately captures natural motion and visual coarticulation efects. Our deep learning approach enjoys several attractive properties: it runs in real-time, requires minimal parameter tuning, generalizes well to novel input speech sequences, is easily edited to create stylized and emotional speech, and is compatible with existing animation retargeting approaches. One important focus of our work is to develop an efective approach for speech animation that can be easily integrated into existing production pipelines. We provide a detailed description of our end-to-end approach, including machine learning design decisions. Generalized speech animation results are demonstrated over a wide range of animation clips on a variety of characters and voices, including singing and foreign language input. Our approach can also generate on-demand speech animation in real-time from user speech input.
REF uses a sliding window approach to animate a parametric face model from phoneme labels.
A deep learning approach for generalized speech animation
TOGS
Computer Science