Adrian Ulises Gonzalez Casillas

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About: 

Currently, he is a Ph.D. student at Cinvestav IPN, unidad Guadalajara, working on Procedural Outfit Generation for Videogame Characters, using both cognitive and machine learning perspectives to address it.

His main research interests include Videogames, Procedural Content Generation, Cognitive Science, Computational Creativity, Generative models, among others.

Research: 

Procedural Outfit Generation for Videogames

Research Abstract: 

The arrival of free-to-play, loot-boxes and other similar videogame business models has had a huge impact in how developers handle software-as-a-service. A common practice is selling cosmetic customizations for in-game content, which are periodically updated based on specific themes or events. However, the capacity of game studios to cope with this demand heavily relies on their art teams. Despite great research on procedural content generation and generative models, the
number of works focused on character design is relatively small and rarely adopted by big studios. We intend to create a tool to support designers in characters outfit creation using characters' contextual information for continuous evaluation of the visual design and to constrain generative recommendations. We describe the main components that a tool for this task should consider and argue that current datasets might be unsuitable for evaluating and generating content for non-realistic games.

Biography: 

As a M.Sc. student at Cinvestav IPN, unidad Guadalajara, he joined the Distributed & Multi-Agents Systems group, in the area of Cognitive Architectures, in 2017. His research addressed the Cognitive Architecture of Visual Perception.

Currently, he is a Ph.D. student at Cinvestav IPN, unidad Guadalajara, working on Procedural Outfit Generation for Videogame Characters, using both cognitive and machine learning perspectives to address it.