Audio Driven Facial Animation by Joint End to End Learning of Pose and Emotion

ACM Transactions on Graphics (Proc. SIGGRAPH 2017) Tero Karras (NVIDIA) Timo Aila (NVIDIA) Samuli Laine (NVIDIA) Antti Herva (Remedy Entertainment) Jaakko Lehtinen (NVIDIA and Aalto University) We present a machine learning technique for driving 3D facial animation by audio input in real time and with low latency. Our deep neural network learns a mapping from input waveforms to the 3D vertex coordinates of a face model, and simultaneously discovers a compact, latent code that disambiguates the variations in facial expression that cannot be explained by the audio alone. During inference, the latent code can be used as an intuitive control for the emotional state of the face puppet. We train our network with 35 minutes of highquality animation data obtained using traditional, visionbased performance capture methods. Even though our primary goal is to model the speaking style of a single actor, our model yields reasonable results