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University of Paris 8

University of Paris 8. Animation improvements and face creation tool for ECAs Nicolas Ech Chafai Benjamin Dariouch Maurizio Mancini Catherine Pelachaud. Overview. aiming at improving agent's facial animation quality: we are studying some motion captured data we apply results to our ECA

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University of Paris 8

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  1. University of Paris 8 • Animation improvements and face creation tool for ECAs • Nicolas Ech Chafai • Benjamin Dariouch • Maurizio Mancini • Catherine Pelachaud

  2. Overview • aiming at improving agent's facial animation quality: • we are studying some motion captured data • we apply results to our ECA • to allow the creation of individualized ECAs: • we developed one tool for MPEG4 face creation • we propose some refinements to MPEG4 specification

  3. MOCAP data analysis • Three main goals: • displacement of FAPs during emotion presentation • synchronization between different FAPs • FAP values during transition between consecutive emotions

  4. Collected MOCAP data • 2 actors • 33 markers, 21 of them are the MPEG4 FAPs • 78 sequences: • basic movements • rising eyebrows • smiling • … • basic emotions • anger • happiness • surprise • …

  5. MOCAP problems • (we discovered that) obtaining usable data is not straightforward • right size and shape markers have to be used • cameras have to be placed properly • data has to be translated to the needed reference system • data has to be filtered from noise

  6. Data example • smile

  7. Video examples • frown clip • file: clips/coline 36 eyebrows • fear clip • file: clips/coline 56 fear

  8. Facial animation model • FAPs displacement during basic emotions • our model was simply based on onset-apex-offset

  9. Data observed model • on real data we observed other general behaviors

  10. Results • we started to introduce the ADSR model: • given a sequence of (phase,intensity,duration) where phase is from {Attack, Decay, Sustain Release}, the FAP curve is built using keyframe Hermite interpolation:

  11. ADSR vs real data

  12. ADSR example • clip

  13. MPEG4 face tool

  14. MPEG4 face tool • imports models from Poser • allows the selection of the areas influenced by FDPs

  15. Tool's features • automatic selection and symmetrization • automatic association region name available FDPs

  16. Example • exports into a data file (containing geometry + regions) readable from the Greta's player

  17. Example • flat.avi (note: female speech)

  18. Added new FAPUs • after adding new faces some refinements for the MPEG4 player will be needed

  19. Example • clip without new FAPUs • clip with new FAPUs

  20. Conclusions • more data has to be captured in proper way • focus more on interaction between different FAPs and transition between sequential expressions • ADSR has to be fully implemented • for documentation, papershttp://www.iut.univ-paris8.fr/greta • for Greta applications available to the other HUMAINE members please contact usm.mancini@iut.univ-paris8.fr

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