Abstract We present a novel method to transfer speech animation recorded in low resolution videos onto realistic 3D facial models. Unsupervised learning is utilized on a speech video corpus to find underlying manifold of facial configurations. K-means clustering is applied on the low dimensional space to find key speaking-related facial shapes. With a small set of laser scanner captured 3D models related to the clustering centroid, the facial animation in 2D videos is transferred onto 3D shapes. Especially by virtue of a weak perspective projection model, the underlying mandible rotation is recovered from videos and is utilized to drive 3D skull movements. The adaption of a generic skull onto facial models is guided by a 2D image, Tissue Map. With parsimonious data requirements, our system realizes the animation transferring and gains a realistic rendering effect with the underlying anatomical structure.


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    Titel :

    Vision Based Speech Animation Transferring with Underlying Anatomical Structure


    Beteiligte:
    Pei, Yuru (Autor:in) / Zha, Hongbin (Autor:in)


    Erscheinungsdatum :

    2006-01-01


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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