Abstract
Character design is a key ingredient to the success of any comic-book, graphic novel, or animated feature. Artists typically use shape, size and proportion as the first design layer to express role, physicality and personality traits. In this paper, we propose a knowledge mining framework that extracts primitive shape features from finished art, and trains models with labeled metadata attributes. The applications are in shape-based query of character databases as well as label-based generation of basic shape scaffolds, providing an informed starting point for sketching new characters. It paves the way for more intelligent shape indexing of arbitrary well-structured objects in image libraries. Furthermore, it provides an excellent tool for novices and junior artists to learn from the experts. We first describe a novel primitive based shape signature for annotating character body-parts. We then use support vector machine to classify these characters using their body part’s shape signature as features. The proposed data transformation is computationally light and yields compact storage. We compare the learning performance of our shape representation with a low-level point feature representation, with substantial improvement.
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Islam, M.T., Nahiduzzaman, K.M., Peng, W.Y., Ashraf, G. (2010). Learning from Humanoid Cartoon Designs. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2010. Lecture Notes in Computer Science(), vol 6171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14400-4_47
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DOI: https://doi.org/10.1007/978-3-642-14400-4_47
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