Google Research

Toward Believable Acting for Autonomous Animated Characters

  • Cassidy J Curtis
  • Sigurdur Orn Adalgeirsson
  • Horia Stefan Ciurdar
  • Peter F Mcdermott
  • JD Velásquez
  • W. Bradley Knox
  • Alonso Martinez
  • Dei Gaztelumendi
  • Norberto Adrian Goussies
  • Tianyu Liu
  • Palash Nandy
Proceedings of the 15th ACM SIGGRAPH Conference on Motion, Interaction and Games (MIG '22), New York, NY, USA (2022), pp. 1-15


This paper describes design principles and a system, based on reinforcement learning and procedural animation, to create an autonomous character capable of believable acting—exhibiting a responsive and expressive illusion of interactive life, grounded in its subjective experience of its world. The design principles incorporate knowledge from animation, human-computer interaction, and psychology, articulating guidelines that, when followed, support a viewer’s suspension of disbelief. The system’s reinforcement learning brain generates action, emotion, and attention signals based on motivational drives, and its procedural animation system translates those signals into expressive biophysical movement in real time. We demonstrate the system on a stylized quadruped character in a virtual habitat. In a user study, participants rated the character favorably on animacy and ability to experience emotions, which is consistent with finding the character believable.

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