Discovering Fatigued Movements for Virtual Character Animation

Noshaba Cheema*, Rui Xu, Nam Hee Kim, Perttu Hämäläinen, Vladislav Golyanik, Marc Habermann, Christian Theobalt, Philipp Slusallek

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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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.
Original languageEnglish
Title of host publicationProceedings - SIGGRAPH Asia 2023 Conference Papers, SA 2023
EditorsStephen N. Spencer
Number of pages12
ISBN (Electronic)979-8-4007-0315-7
Publication statusPublished - 11 Dec 2023
MoE publication typeA4 Conference publication
EventSIGGRAPH Asia - Sydney, Australia
Duration: 12 Dec 202315 Dec 2023


ConferenceSIGGRAPH Asia


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