Remagicmirror: Action learning using human reenactment with the mirror metaphor

Fabian Lorenzo Dayrit, Ryosuke Kimura, Yuta Nakashima, Ambrosio Blanco, Hiroshi Kawasaki, Katsushi Ikeuchi, Tomokazu Sato, Naokazu Yokoya

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationMultiMedia Modeling - 23rd International Conference, MMM 2017, Proceedings
EditorsLaurent Amsaleg, Gylfi Thór Gudmundsson, Cathal Gurrin, Björn Thór Jónsson, Shin’ichi Satoh
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9783319518107
Publication statusPublished - 2017
Externally publishedYes
Event23rd International Conference on MultiMedia Modeling, MMM 2017 - Reykjavik, Iceland
Duration: Jan 4 2017Jan 6 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10132 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other23rd International Conference on MultiMedia Modeling, MMM 2017

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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