Transferable videorealistic speech animation

  • Authors:
  • Yao-Jen Chang;Tony Ezzat

  • Affiliations:
  • Computer and Communications Laboratories, ITRI, Taiwan;Center for Biological and Computational Learning, MIT

  • Venue:
  • Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation
  • Year:
  • 2005

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Abstract

Image-based videorealistic speech animation achieves significant visual realism at the cost of the collection of a large 5- to 10-minute video corpus from the specific person to be animated. This requirement hinders its use in broad applications, since a large video corpus for a specific person under a controlled recording setup may not be easily obtained In this paper, we propose a model transfer and adaptation algorithm which allows for a novel person to be animated using only a small video corpus. The algorithm starts with a multidimensional morphable model (MMM) previously trained from a different speaker with a large corpus, and transfers it to the novel speaker with a much smaller corpus. The algorithm consists of 1) a novel matching-by-synthesis algorithm which semi-automatically selects new MMM prototype images from the new video corpus and 2) a novel gradient descent linear regression algorithm which adapts the MMM phoneme models to the data in the novel video corpus. Encouraging experimental results are presented in which a morphable model trained from a performer with a 10-minute corpus is transferred to a novel person using a 15-second movie clip of him as the adaptation video corpus.