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Measurement and animation of the eye region of the human face in reduced coordinates

University of British Columbia

The goal of this dissertation is to develop methods to measure, model, and animate facial tissues of the region around the eyes, referred to as the eye region. First, we measure the subtle movements of the soft tissues of the eye region using a monocular RGB-D camera setup, and second, we model and animate these movements using parameterized motion models. The muscles and skin of the eye region are very thin and sheetlike. By representing these tissues as thin elastic sheets in reduced coordinates, we have shown how we can measure and animate these tissues efficiently. To measure tissue movements, we optically track both eye and skin motions using monocular video sequences. The key idea here is to use a reduced coordinates framework to model thin sheet-like facial skin of the eye region. This framework implicitly constrains skin to conform to the shape of the underlying object when it slides. The skin configuration can then be efficiently reconstructed in 3D by tracking two dimensional skin features in video. This reduced coordinates model allows interactive real-time animation of the eye region in WebGL enabled devices using a small number of animation parameters, including gaze. Additionally, we have shown that the same reduced coordinates framework can also be used for physics-based simulation of the facial tissue movements and to produce tissue deformations that occur in facial expressions. We validated our skin measurement and animation algorithms using skin movement sequences with known skin motions, and we can recover skin sliding motions with low reconstruction errors. We also propose an image-based algorithm that corrects accumulated inaccuracy of standard 3D anatomy registration systems that occurs during motion capture, anatomy transfer, image generation, and animation. After correction, we can overlay the anatomy on input video with low misalignment errors for augmented reality applications, such as anatomy mirroring. Our results show that the proposed image-based corrective registration can effectively reduce these inaccuracies.

Text

2018-06-14

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/66300

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/