  What's the most accurate 3D model of your face you can obtain while sitting
at your desk? We attempt to answer this question in our work. High fidelity
face reconstructions have so far been limited to either studio settings or
through expensive 3D scanners. On the other hand, unconstrained reconstruction
methods are typically limited by low-capacity models. Our method reconstructs
accurate face geometry of a subject using a video shot from a smartphone in an
unconstrained environment. Our approach takes advantage of recent advances in
visual SLAM, keypoint detection, and object detection to improve accuracy and
robustness. By not being constrained to a model subspace, our reconstructed
meshes capture important details while being robust to noise and being
topologically consistent. Our evaluations show that our method outperforms
current single and multi-view baselines by a significant margin, both in terms
of geometric accuracy and in capturing person-specific details important for
making realistic looking models.
