Constructing Human Motion Manifold With Sequential Networks

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dc.contributor.authorJang, Deok‐Kyeongen_US
dc.contributor.authorLee, Sung‐Heeen_US
dc.contributor.editorBenes, Bedrich and Hauser, Helwigen_US
dc.date.accessioned2020-10-06T16:54:02Z
dc.date.available2020-10-06T16:54:02Z
dc.date.issued2020
dc.description.abstractThis paper presents a novel recurrent neural network‐based method to construct a latent motion manifold that can represent a wide range of human motions in a long sequence. We introduce several new components to increase the spatial and temporal coverage in motion space while retaining the details of motion capture data. These include new regularization terms for the motion manifold, combination of two complementary decoders for predicting joint rotations and joint velocities and the addition of the forward kinematics layer to consider both joint rotation and position errors. In addition, we propose a set of loss terms that improve the overall quality of the motion manifold from various aspects, such as the capability of reconstructing not only the motion but also the latent manifold vector, and the naturalness of the motion through adversarial loss. These components contribute to creating compact and versatile motion manifold that allows for creating new motions by performing random sampling and algebraic operations, such as interpolation and analogy, in the latent motion manifold.en_US
dc.description.number6
dc.description.sectionheadersArticles
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume39
dc.identifier.doi10.1111/cgf.14028
dc.identifier.issn1467-8659
dc.identifier.pages314-324
dc.identifier.urihttps://doi.org/10.1111/cgf.14028
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14028
dc.publisher© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltden_US
dc.subjectmotion capture
dc.subjectanimation
dc.subjectbehavioural animation
dc.subjectanimation
dc.titleConstructing Human Motion Manifold With Sequential Networksen_US
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