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ReMagicMirror: Action Learning Using Human Reenactment with the Mirror Metaphor

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dc.contributor.author Kimura, Ryosuke en
dc.contributor.author Nakashima, Yuta en
dc.contributor.author Blanco, Ambrosio en
dc.contributor.author Kawasaki, Hiroshi en
dc.contributor.author Ikeuchi, Katsushi en
dc.contributor.author Sato, Tomokazu en
dc.contributor.author Yokoya, Naokazu en
dc.date.accessioned 2018-03-28T05:33:58Z en
dc.date.available 2018-03-28T05:33:58Z en
dc.date.issued 2016-12-31 en
dc.identifier.isbn 978-3-319-51811-4 en
dc.identifier.uri http://hdl.handle.net/10061/12231 en
dc.description MMM 2017: 23rd International Conference on Multimedia Modeling, Jan 4-6, 2017, Reykjavik, Iceland, en
dc.description.abstract We propose ReMagicMirror, a system to help people learn actions (e.g., martial arts, dances). We first capture the motions of a teacher performing the action to learn, using two RGB-D cameras. Next, we fit a parametric human body model to the depth data and texture it using the color data, reconstructing the teacher’s motion and appearance. The learner is then shown the ReMagicMirror system, which acts as a mirror. We overlay the teacher’s reconstructed body on top of this mirror in an augmented reality fashion. The learner is able to intuitively manipulate the reconstruction’s viewpoint by simply rotating her body, allowing for easy comparisons between the learner and the teacher. We perform a user study to evaluate our system’s ease of use, effectiveness, quality, and appeal. ja
dc.language.iso en en
dc.publisher Springer International Publishing en
dc.rights Copyright c 2017 Springer International Publishing ja
dc.subject 3D human reconstruction en
dc.subject Human reenactment en
dc.subject RGB-D sensors en
dc.title ReMagicMirror: Action Learning Using Human Reenactment with the Mirror Metaphor en
dc.type.nii Conference Paper en
dc.textversion author en
dc.identifier.spage 303 en
dc.identifier.epage 315 en
dc.relation.doi 10.1007/978-3-319-51811-4_25 en
dc.identifier.NAIST-ID 73298663 en
dc.identifier.NAIST-ID 73292633 en
dc.identifier.NAIST-ID 73292286 en


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