November 27, 2018
Deep Incremental Learning for Efficient High-Fidelity Face Tracking
ACM SIGGRAPH ASIA 2018
In this paper, we present an incremental learning framework for efficient and accurate facial performance tracking. Our approach is to alternate the modeling step, which takes tracked meshes and texture maps to train our deep learning-based statistical model, and the tracking step, which takes predictions of geometry and texture our model infers from measured images and optimize the predicted geometry by minimizing image, geometry and facial landmark errors.
By: Chenglei Wu, Takaaki Shiratori, Yaser Sheikh