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.

MIG '22 Talk


  author = {Curtis, Cassidy and Adalgeirsson, Sigurdur Orn and Ciurdar, Horia Stefan and McDermott, Peter and Vel\'{a}squez, JD and Knox, W. Bradley and Martinez, Alonso and Gaztelumendi, Dei and Goussies, Norberto Adrian and Liu, Tianyu and Nandy, Palash},
  title = {Toward Believable Acting for Autonomous Animated Characters},
  year = {2022},
  isbn = {9781450398886},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {},
  doi = {10.1145/3561975.3562941},
  booktitle = {Proceedings of the 15th ACM SIGGRAPH Conference on Motion, Interaction and Games},
  articleno = {1},
  numpages = {15},
  keywords = {synthetic characters, believability, acting, reinforcement learning, animation, autonomous characters},
  location = {Guanajuato, Mexico},
  series = {MIG '22}