1. Facial Type, Expression, and Viseme Generation Josh McCoy, James Skorupski, and Jerry Yee

2. Introduction Virtual Human Faces Hard to generate Easy to criticize Motivation Movies Games Problems Hand-made models take time Captured face scans Expensive Time consuming Physically-based complex sometimes weird

3. Contribution Hybrid Method Sparse Data + Geometric Analysis User-guided categorization Lots of variation => easy, few controls

4. Related Work: Single Axis Variation V. Blanz, C. Basso, and T. Vetter Reanimating Faces in Images and Video. Variation along the type axis: skinny to heavy. Variation along the expression axis: frown to smile

5. Related Work: Multi-Axes Variation E. Chuang and C. Bregler Mood Swings: Expressive Speech Animation Variation along expression and viseme axes. D. Vlasic, M. Brand, H. Pfister, & J. Popovic Face Transfer with Multilinear Models Variation along type, expression, and viseme axes.

6. Methods Tagging Analysis Generation

7. Tagging Three data sets are needed to fill the model space Set of many neutral faces Set of one face in many poses Set of Visemes with reference face User “tags” each face with attribute value

8. Tagging

9. Analyze Get Offsets From Average Face Get Offsets From Average Face

10. Analyze PCA on offsets Get weights for all faces

11. Analyze Form Weight Matrix W Form Attribute Matrix A

12. Analyze Find Three Transform Matrices for Type Expression Viseme

13. Generation Put requested attributes into vector Get eigenvector weights

14. Generation Make New Geometry

15. Results, Weaknesses, Conclusion Demo! Demo!

16. Future Work Enforce physically-based geometry constraints More complex geometry analysis Integration into game engine