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dc.contributor.authorFrezzato, Anthonyen_US
dc.contributor.authorTangri, Arshen_US
dc.contributor.authorAndrews, Sheldonen_US
dc.contributor.editorDominik L. Michelsen_US
dc.contributor.editorSoeren Pirken_US
dc.description.abstractWe propose a method for synthesizing get-up motions for physics-based humanoid characters. Beginning from a supine or prone state, our objective is not to imitate individual motion clips, but to produce motions that match input curves describing the style of get-up motion. Our framework uses deep reinforcement learning to learn control policies for the physics-based character. A latent embedding of natural human poses is computed from a motion capture database, and the embedding is furthermore conditioned on the input features. We demonstrate that our approach can synthesize motions that follow the style of user authored curves, as well as curves extracted from reference motions. In the latter case, motions of the physics-based character resemble the original motion clips. New motions can be synthesized easily by changing only a small number of controllable parameters. We also demonstrate the success of our controllers on rough and inclined terrain.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Computing methodologies --> Physical simulation; Theory of computation --> Reinforcement learning
dc.subjectComputing methodologies
dc.subjectPhysical simulation
dc.subjectTheory of computation
dc.subjectReinforcement learning
dc.titleSynthesizing Get-Up Motions for Physics-based Charactersen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersMotion II
dc.identifier.pages12 pages

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  • 41-Issue 8
    ACM SIGGRAPH / Eurographics Symposium on Computer Animation 2022

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