Learning Character Design from Experts and Laymen

Abstract

The use of pose and proportion to represent character traits is well established in art and psychology literature. However, there are no Golden Rules that quantify a generic design template for stylized character figure drawing. Given the wide variety of drawing styles and a large feature dimension space, it is a significant challenge to extract this information automatically from existing cartoon art. This paper outlines a game-inspired methodology for systematically collecting layman perception feedback, given a set of carefully chosen trait labels and character silhouette images. The rated labels were clustered and then mapped to the pose and proportion parameters of characters in the dataset. The trained model can be used to classify new drawings, providing valuable insight to artists who want to experiment with different poses and proportions in the draft stage. The proposed methodology was implemented as follows: 1) Over 200 full-body, front-facing character images were manually annotated to calculate pose and proportion, 2) A simplified silhouette was generated from the annotations to avoid copyright infringements and prevent users from identifying the source of our experimental figures, 3) An online casual role-playing puzzle game was developed to let players choose meaningful tags (role, physicality and personality) for characters, where tags and silhouettes received equitable exposure, 4) Analysis on the generated data was done both in stereotype label space as well as character shape space, 5) Label filtering and clustering enabled dimension reduction of the large description space, and subsequently, a select set of design features were mapped to these clusters to train a neural network classifier. The mapping between the collected perception and shape data give us quantitative and qualitative insight into character design. It opens up applications for creative reuse of (and deviation from) existing character designs.