English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Poster

Recognizing your own motions on virtual avatars: is it me or not?

MPS-Authors
/persons/resource/persons192772

Wellerdiek,  AC
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84053

Leyrer,  M
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84285

Volkova,  E
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons83854

Chang,  D-S
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84088

Mohler,  BJ
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Wellerdiek, A., Leyrer, M., Volkova, E., Chang, D.-S., & Mohler, B. (2013). Recognizing your own motions on virtual avatars: is it me or not?. Poster presented at ACM Symposium on Applied Perception (SAP '13), Dublin, Ireland.


Cite as: http://hdl.handle.net/21.11116/0000-0001-5515-8
Abstract
Most of the time point-light figures are used for motion-recognition, which present motions by only displaying the moving joints of the actor. In this study we were interested in whether self-recognition of motion changes with different representations. First, we captured participants' motions and remapped them on a point-light figure and a male and female virtual avatar. In the second part the same participants were asked to recognize their own motions on all three representations. We found that the recognition rate for own motions is high across all representations and different actions. The recognition rate was better on the point-light figure, despite being perceived as most difficult from the participants. The gender of the visual avatar did not matter in self-recognition.