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MikeTalk:An Adaptive Man-Machine Interface

MikeTalk:An Adaptive Man-Machine Interface. Tony Ezzat Volker Blanz Tomaso Poggio. TTVS Overview. Input: Text Output: Photo-realistic talking face uttering text. Desktop Agents. You have received 1 email from Tommy Poggio. Desktop Agents. Customer Support. You have bought 20

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MikeTalk:An Adaptive Man-Machine Interface

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  1. MikeTalk:An Adaptive Man-Machine Interface Tony Ezzat Volker Blanz Tomaso Poggio

  2. TTVS Overview • Input: Text • Output: Photo-realistic talking face uttering text

  3. Desktop Agents

  4. You have received 1 email from Tommy Poggio. Desktop Agents

  5. Customer Support

  6. You have bought 20 shares of SONY at $40 each. Customer Support

  7. Advertisements

  8. Hi Tony, would you be interested in a ticket from Boston to New York for $50.00? Advertisements

  9. Modules

  10. Phoneme Corpus Step 1: • collect a visual corpus from a subject • corpus contains 44 words • one word for each American English phoneme

  11. 6 Consonantal Visemes Step 2: • extract one image per phoneme: viseme • group visemes together by visual similarity

  12. 9 Vocalic Visemes (+ 1 SilenceViseme)

  13. Problem1:Need to Interpolate!

  14. Solution: Morphing! Simultaneous interpolation of shape & texture. (Beier & Neely 1992) Problem 2: too tedious to specify correspondence by hand across many images!

  15. Solution: Optical Flow (Horn & Schunk 1986) (Lucas & Kanade 1988) • To interpolate between two visemes, optical flow is first computed • A 2D motion vector field is produced: dx(x,y) dy(x,y)

  16. Morphing • Forward warping A to B • Forward warping B to A • Blending • Holefilling

  17. Synthesis Database • 16 Visemes total • 256 Optical flow vectors total, from every viseme to every other viseme

  18. Concatenation and Lip Sync • Load the correct viseme transitions • Concatenate viseme transitions • Sample the viseme transitions using audio durations

  19. Examples “1, 2, 3, 4, 5” “you have received 10 email messages.” “cat, dog, pig, cow, moose, horse, sheep”

  20. Current Work • Coarticulation • Eye + head movements • Emotion • 3D instead of 2d • Psychophysics

  21. 3D With Volker Blanz

  22. The End

  23. Co-articulation • Problem: Current method does not handle coarticulation, so speech looks overly articulated • Can record all possible triphones/ quadriphones but this approach requires a lot of data! • Best method is to learn a model for coarticulation, but what is the representation for the lips?

  24. Principal Components Analysis • Each image is a vector in a high-dimensional space • Using PCA, find the optimal set of vectors that span the space • Project the entire corpus onto those basis vectors

  25. Top 2 PCA Bases for /buut/

  26. Top 2 PCA Bases for /get/ Problem: Too nonlinear!

  27. Flow Component Analysis • Compute optical from a reference lip image to all other images in the corpus • Compute PCA on all the flows

  28. Top 2 FPCA Bases for /buut/

  29. Top 2 FPCA Bases for /get/ Much more linear behavior!

  30. Current Work • Now that we have parameterized the mouth, what is the model for mouth synthesis? • How is that model fit to the PCA data?

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