  We introduce the concept of unconstrained real-time 3D facial performance
capture through explicit semantic segmentation in the RGB input. To ensure
robustness, cutting edge supervised learning approaches rely on large training
datasets of face images captured in the wild. While impressive tracking quality
has been demonstrated for faces that are largely visible, any occlusion due to
hair, accessories, or hand-to-face gestures would result in significant visual
artifacts and loss of tracking accuracy. The modeling of occlusions has been
mostly avoided due to its immense space of appearance variability. To address
this curse of high dimensionality, we perform tracking in unconstrained images
assuming non-face regions can be fully masked out. Along with recent
breakthroughs in deep learning, we demonstrate that pixel-level facial
segmentation is possible in real-time by repurposing convolutional neural
networks designed originally for general semantic segmentation. We develop an
efficient architecture based on a two-stream deconvolution network with
complementary characteristics, and introduce carefully designed training
samples and data augmentation strategies for improved segmentation accuracy and
robustness. We adopt a state-of-the-art regression-based facial tracking
framework with segmented face images as training, and demonstrate accurate and
uninterrupted facial performance capture in the presence of extreme occlusion
and even side views. Furthermore, the resulting segmentation can be directly
used to composite partial 3D face models on the input images and enable
seamless facial manipulation tasks, such as virtual make-up or face
replacement.
