A Framework for Capture and Synthesis of <br>High Resolution Facial Geometry and Performance
A Framework for Capture and Synthesis of
High Resolution Facial Geometry and Performance
Wan-Chun Ma
PhD Thesis, National Taiwan University



We present a framework that captures and synthesizes high resolution facial geometry and performance. In order to capture highly detailed surface structures, a theory of fast normal recovery using spherical gradient illumination patterns is presented to estimate surface normal maps of an object from either its diffuse or specular reflectance, simultaneously from any viewpoints. We show that the normal map from specular reflectance yields the best record of detailed surface shape, which can be used for geometry enhancement. Moreover, the normal map from the diffuse reflectance is able to produce a good approximation of subsurface scattering. Based on the theory, two systems are developed to capture high resolution facial geometry of a static face or dynamic facial performance.


We human beings are able to exhibit complex facial expressions. On the other hand, we are also evolved to be extremely sensitive to reading subtle changes in facial expressions. As an result, this leads to the concept of the Uncanny Valley theory, which holds that the closer something appears to human, the more its dissimilarities may stand out and create a negative reaction in viewers. We tend to become less tolerate to facial animations as they are getting closer to realism. Therefore, accurately reproducing facial expressions still remains one of the most difficult problem in computer graphics.

To make facial animation looks plausible, at least we need to consider the following aspects:



PhD Thesis Paper:

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