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CPSC 689-603: Data-driven Computer Graphics Jinxiang Chai. Compute Graphics. Traditional Graphics Versus Data-driven Graphics. Lighting. Geometry. texture. Surface property. Motion. Traditional Graphics. Conceptual world. Modeling. Simulation. Traditional Graphics. Conceptual world.
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Traditional Graphics Versus Data-driven Graphics Lighting Geometry texture Surface property Motion
Traditional Graphics Conceptual world Modeling Simulation
Traditional Graphics Conceptual world Modeling Simulation shape models reflection models motion models
Traditional Graphics Conceptual world Modeling Simulation
Traditional Graphics Conceptual world Modeling Simulation Pros: + Compact representation + Easy to manipulate Cons: - Very hard to build realistic models - Too complex to simulate
Data-driven Graphics Data capture Real world Data analysis and synthesis Pros: + High realism + Computer cost independent on the complexity of the model Cons: - Large set of data - Hard to control, edit, modify
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
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
Thesis Research Goal: everyone can generate and control human animation easily and quickly Online animation control
Thesis Research Goal: everyone can generate and control human animation easily and quickly Online animation control
Thesis Research Goal: everyone can generate and control human animation easily and quickly Offline animation control User input Output animation
Thesis Research Goal: everyone can generate and control human animation easily and quickly Offline animation control User input Output animation
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.)
Grading Schemes Paper presentation (20%) Class participation/discussion (20%) Paper summary (20%) Final project (40%)
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
Class Participation/Discussion Show up Do the reading Submit the paper summary to me BEFORE the class Actively participate in paper discussion
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
Grading Schemes Paper presentation (20%) Class participation/discussion (20%) Paper summary (20%) Final project (40%)
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
Other Information My email: jchai@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/
Email Me Today Your background • Graphics? • Math? • Coding? Your research Interest? Master/Ph.D. (year)? Why do you take this class?