1. CPSC 689-603: Data-driven Computer Graphics Jinxiang Chai

2. Compute Graphics

3. Traditional Graphics Versus Data-driven Graphics Lighting Geometry Motion texture Surface property

4. Conceptual world ModelingSimulation Traditional Graphics

5. Conceptual world ModelingSimulation Traditional Graphics shape modelsreflection models motion models

6. Conceptual world ModelingSimulation Traditional Graphics

7. Conceptual world ModelingSimulation Traditional Graphics Pros: + Compact representation + Easy to manipulate Cons: - Very hard to build realistic models - Too complex to simulate

8. Data-driven Graphics

9. Real world Data capture Data analysis and synthesis Data-driven Graphics Pros: + High realism + Computer cost independent on the complexity of the model Cons: - Large set of data - Hard to control, edit, modify

10. What You Will Learn An in-depth study of data-driven computer graphics Learn how to find and formulate a research problem Refine your presentation skill

11. My Research Interest Interested in animation, graphics, and vision Methods for creating and manipulating high-dimensional visual media (animation, models, images, and videos) Data-driven approach Video-based data capture Thesis: exploiting spatial-temporal constraints for interactive animation control

12. Thesis Research Goal: everyone can generate and control human animation easily and quickly Online animation control

13. Thesis Research Goal: everyone can generate and control human animation easily and quickly Online animation control

14. Thesis Research Goal: everyone can generate and control human animation easily and quickly Offline animation control User input Output animation

15. Thesis Research Goal: everyone can generate and control human animation easily and quickly Offline animation control User input Output animation

16. Prerequisites A good working knowledge of C/C++ or Matlab A good understand of math (linear algebra, probability theory ) Background in CG Willing to learn new stuffs (optimization, statistical learning, computer vision, etc.)

17. Grading Schemes Paper presentation (20%) Class participation/discussion (20%) Paper summary (20%) Final project (40%)

18. Paper Presentation Before the talk Visit the project webpage Download the video or ask me for the video Give 20 -- 25 minutes talk Lead the paper discussion Come to my office hours if u need help

19. Class Participation/Discussion Show up Do the reading Submit the paper summary to me BEFORE the class Actively participate in paper discussion

20. Final Project Approved by the professor Student can work in a group of two Submit your code and final project report Talk to me if you need any helps Late policy: 20% reduction per day if you do not have good reasons

21. Grading Schemes Paper presentation (20%) Class participation/discussion (20%) Paper summary (20%) Final project (40%)

22. Chai ’ s Talk/Paper Style Introduction What? Why? How? Related work or background Algorithm overview Describe each step of the algorithm Experiments & results Discussion & future work

23. Other Information My email: jchai@cs.tamu.edujchai@cs.tamu.edu My homepage: http://faculty.cs.tamu.edu/jchai My office: Rm 527D Bright Office hours: MW 4:00-5:00 Pm Course webpage: http://www.cs.tamu.edu/jchai/689_DRCG/

24. Email Me Today Your background Graphics? Math? Coding? Your research Interest? Master/Ph.D. (year)? Why do you take this class?