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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 4 of 4 1 Animating blendshape faces by cross mapping motion capture data Zhigang Deng In: http://graphics.usc.edu/cgit/pdf/papers/i3d_anim.pdf (2006) BASE Show details 2 Animating blendshape faces by cross mapping motion capture data Zhigang Deng In: http://www-scf.usc.edu/~peiyingc/i3d_anim_preprint.pdf (2006) BASE Show details 3 Animating blendshape faces by cross mapping motion capture data Zhigang Deng In: http://graphics.cs.uh.edu/publication/pub/I3D_anim_preprint.pdf (2006) BASE Show details 4 ABSTRACT Synthesizing Speech Animation By Learning Compact Speech Co-Articulation Models Zhigang Deng; J. P. Lewis; Ulrich Neumann In: http://graphics.cs.uh.edu/publication/pub/CGI-speech.pdf Abstract: Figure 1: Some synthesized frames of singing a part of “hunter ” music (by dido). While speech animation fundamentally consists of a sequence of phonemes over time, sophisticated animation requires smooth interpolation and co-articulation effects, where the preceding and following phonemes influence the shape of a phoneme. Coarticulation has been approached in speech animation research in several ways, most often by simply smoothing the mouth geometry motion over time. Data-driven approaches tend to generate realistic speech animation, but they need to store a large facial motion database, which is not feasible for real time gaming and interactive applications on platforms such as PDAs and cell phones. In this paper we show that accurate speech co-articulation model with compact size can be learned from facial motion capture data. An initial phoneme sequence is generated automatically from Text-To-Speech (TTS) systems. Then, our learned co-articulation model is applied to the resulting phoneme sequence, producing natural and detailed motion. The contribution of this work is that speech coarticulation models “learned ” from real human motion data can be used to generate natural-looking speech motion while simultaneously preserving the expressiveness of the animation via keyframing control. Simultaneously, this approach can be effectively applied to interactive applications due to its compact size. Keyword: Data-Driven; Dynamic Programming; Facial Animation; Keyframing Control; Motion Capture; Speech Animation; Speech Co-Articulation URL: http://graphics.cs.uh.edu/publication/pub/CGI-speech.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.81.8732 BASE Hide details

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