Geometric Methods for Realistic Animation of Faces

Realistic facial synthesis is one of the most fundamental problems in computer graphics, and has been sought after for approximately four decades. It is desired in a wide variety of fields, such as character animation for films and advertising, computer games, video teleconferencing, user-interface agents and avatars, and facial surgery planning. Humans, on the other hand, are experts in identifying every detail and every regularity or variation in proportion from one individual to the next. The task of creating a realistic human face is elusive due to this, as well as many other factors. Among which are complex surface details, spatially and temporally varying skin texture and subtle emotions that are conveyed through even more subtle motions. In this thesis, we present the most commonly practiced facial content creation process, and contribute to the quality of each of its steps. The proposed algorithms significantly increase the level of realism attained by each step and therefore substantially reduce the amount of manual labor required for production quality facial content. The thesis contains three parts, each contributing to one step of the facial content creation pipeline. In the first part, we aim at greatly increasing the fidelity of facial performance captures, and present the first method for detailed spatio-temporal reconstruction of eyelids. Easily integrable with existing high quality facial performance capture approaches, this method generates a person-specific, time-varying eyelid reconstruction with anatomically plausible deformations. Our approach is to combine a geometric deformation model with image data, leveraging multi-view stereo, optical flow, contour tracking and wrinkle detection from local skin appearance. Our deformation model serves as a prior that enables reconstruction of eyelids even under strong self-occlusions caused by rolling and folding skin as the eye opens and closes. In the second part, we contribute to the authoring step of the creation process. We present a method for adding fine-scale details and expressiveness to lowresolution art-directed facial performances. Employing a high-resolution facial performance capture system, we augment artist friendly content, such as those created manually using a rig, via marker-based capture, by fitting a morphable model to a video, or through Kinect-based reconstruction. From the high fidelity