June 16, 2019
Self-Supervised Adaptation of High-Fidelity Face Models for Monocular Performance Tracking
Conference on Computer Vision and Pattern Recognition (CVPR)
Improvements in data-capture and face modeling techniques have enabled us to create high-fidelity realistic face models. However, driving these realistic face models requires special input data, e.g. 3D meshes and unwrapped textures. Also, these face models expect clean input data taken under controlled lab environments, which is very different from data collected in the wild. All these constraints make it challenging to use the high-fidelity models in tracking for commodity cameras. In this paper, we propose a self-supervised domain adaptation approach to enable the animation of high-fidelity face models from a commodity camera.
By: Jae Shin Yoon, Takaaki Shiratori, Shoou-I Yu, Hyun Soo Park