Learning to Generate 3D Stylized Character Expressions from Humans

IEEE Winter Conference on Applications of Computer Vision (WACV)

Publication date: March 12, 2018

Deepali Aneja, Bindita Chaudhuri, Alex Colburn, Gary Faigin, Linda G. Shapiro, Barbara Mones, Aneja

We present ExprGen, a system to automatically generate 3D stylized character expressions from humans in a perceptually valid and geometrically consistent manner. Our multi-stage deep learning system utilizes the latent variables of human and character expression recognition convolutional neural networks to control a 3D animated character rig. This end-to-end system takes images of human faces and generates the character rig parameters that best match the human's facial expression. ExprGen generalizes to multiple characters, and allows expression transfer between characters in a semi-supervised manner. Qualitative and quantitative evaluation of our method based on Mechanical Turk tests show the high perceptual accuracy of our expression transfer results.

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