Discovering Fatigued Movements for Virtual Character Animation

Abstract

Virtual character animation and movement synthesis have advanced rapidly during recent years, especially through a combination of extensive motion capture datasets and machine learning. A remaining challenge is interactively simulating characters that fatigue when performing extended motions, which is indispensable for the realism of generated animations. However, capturing such movements is problematic, as performing movements like backflips with fatigued variations up to exhaustion raises capture cost and risk of injury. Surprisingly, little research has been done on faithful fatigue modeling. To address this, we propose a deep reinforcement learning-based approach, which -- for the first time in literature -- generates control policies for full-body physically simulated agents aware of cumulative fatigue. For this, we first leverage Generative Adversarial Imitation Learning (GAIL) to learn an expert policy for the skill; Second, we learn a fatigue policy by limiting the generated constant torque bounds based on endurance time to non-linear, state- and time-dependent limits in the joint-actuation space using a Three-Compartment Controller (3CC) model. Our results demonstrate that agents can adapt to different fatigue and rest rates interactively, and discover realistic recovery strategies without the need for any captured data of fatigued movement.

Fatigue Synthesis

Citation

@inproceedings{cheema2023fatigue,
  title = {Discovering Fatigued Movements for Virtual Character Animation},
  author = {Cheema, Noshaba and Xu, Rui and Kim, Nam Hee and H{\"a}m{\"a}l{\"a}inen, Perttu and Golyanik, Vladislav and Habermann, Marc and Theobalt, Christian and Slusallek, Philipp},
  booktitle={ACM SIGGRAPH 2023 Conference Proceedings},
  year = {2023}
}

Noshaba Cheema

Rui Xu

Nam Hee Kim

Perttu Hämäläinen

Vladislav Golyanik

Marc Habermann

Christian Theobalt

Philipp Slusallek

Abstract

Fatigue Synthesis

Citation