Creating an Anthropomorphic Folktale Animal: A Pilot Study on Character Design Creativity Derived From Autonomous Behavior Generation Powered by Reinforcement Learning

Abstract

Popular in fantasy films, games, and extended reality, anthropomorphic animals often rely on animator creativity and real animal observation for behavior visualization. This artistic approach captures emotional traits but lacks uncovering diverse, unanticipated behaviors beyond creators' concepts. To enrich character design, this study employs reinforcement learning (RL) agent simulation to explore the autonomous behavior and unexpected responses of the nine-tailed Fox Sister from Korean folklore. As a method, the agent, with a physics-based controller and skeletal joints, uses hybrid action control to transition between bipedal and quadrupedal actions based on the environment. In result, RL character frequently exhibits behavioral shifts, including unexpected actions in response to training steps and terrain complexities like slopes and hurdles, distinguishing them from animation-based finite-state machines. Additionally, this study validates impacts of RL character on character design creativity. To investigate such unknown impacts, this study conducts a comparative pilot study that recruits five character designers under use and nonuse scenario of RL character. Analysis indicates that RL character promotes creativity of character design, conceptualization, and development of scenario and character's attribute. This study highlights RL's potential for visualizing diverse inspirational behaviors of folkloric creatures by simulating interactions between body structure, motion, and environment.