Event Location: GHC 8102

Abstract: Human observers are adept at detecting anomalies in realistic computer-generated (CG) facial animations. With an increased demand for CG characters in education and entertainment applications, it is important to animate accurate, realistic facial expressions. In this thesis, we develop a framework to explore representations of two key facial expressions: blinks and smiles.

We argue that data-driven models of facial deformations are needed to create realistic animations. First, we record large collections of high-resolution dynamic expressions through video and motion capture technology. Next, we build expression-specific models of the dynamic data properties. We explore variants of the model and assess whether viewers perceive the models as more natural than the simplified models present in the literature.

In the first part of the thesis, we build a generative model of the characteristic dynamics of blinks: fast closing of the eyelids followed by a slow opening. Blinks have a characteristic profile with relatively little variation across instances or people. Our results demonstrate the need for an accurate model of eye blink dynamics rather than simple approximations, as viewers perceive the difference.

In the second part of the thesis, we investigate how spatial and temporal linearities impact smile genuineness and build a model for genuine smiles. Our perceptual results indicate that a smile model needs to preserve temporal information. With this model, we synthesize perceptually genuine smiles that outperform traditional animation methods accompanied by plausible head motions.

In the last part of the thesis, we investigate how blinks synchronize with the start and end of spontaneous smiles. Our analysis shows that eye blinks correlate with the end of the smile and occur before the lip corners stop moving downwards. We argue that the timing of blinks relative to smiles is useful in creating compelling facial expressions.

Our work is directly applicable to current methods in animation. For example, we illustrate how our models can be used in the popular framework of blendshape animation to increase realism while keeping the system complexity low. Furthermore, our perceptual results can inform the design of realistic animation systems by highlighting common assumptions that over-simplify the dynamics of expressions.

Committee:Jessica K. Hodgins, Chair

Jeffrey F. Cohn

Nancy Pollard

Carol O’Sullivan, Disney Research and Trinity College, Dublin