animation tips for smiles

The tendency to exaggerate lid tightener-like qualities during moments intended to express happiness or sentimentalism results in a look a little too close to the infamous “smizing” expression. Such a look may please Tyra Banks, but this isn’t America’s Next Top Model.

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faces you don’t want to see during UX research – especially for VR

facial expressions you to avoid during UX research sessions

Faces of discomfort often followed headset adjustment – or predicted upcoming adjustments. Bored faces and faces on the contempt spectrum tended to be predictive of undesirable experiences later disclosed during the post-demo interviews. These expressions were not just useful for predicting events. They also served as points for further investigation.

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faking aging in characters

The Falcon and the Winter Soldier - Carl Lumbly actor

If you are aging a face, pay attention to where you add sagging, deep lines, and folds. There are patterns to follow. While everyone’s pattern is different, general principles still exist. Aging reflects many things – our unique anatomy, our repeated expression use, our past injuries, etc. It is a map of our history.

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killer smiles: a fine line between creepy and beautiful

serial killer smiles - Ted Bundy - Rodney Alcala - compared to James Franco and Willem Dafoe

From observing trends in art, social media, ranking systems, and pop culture, there appear to be two main types of “creepy smiles”: Type I, which I coined The Grinch Pinch and Type 2, which I coined The Muted Shark. Types I and II typically contain all or many of the following features:

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bias in emotion tracking

AU23 - lip tightener - Facial Action Coding System - FACS

We seem to subscribe to the popular oversimplification that machines are less biased than humans; however, if you are familiar with the ways in which machines are trained to read and focus on different aspects of data, you will know: It’s just not that simple.

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