cpsc 689-603: data-driven computer graphics jinxiang chai
TRANSCRIPT
CPSC 689-603:Data-driven Computer Graphics
Jinxiang Chai
Compute Graphics
Traditional Graphics Versus Data-driven Graphics
Lighting
Geometry
Motion
texture
Surface property
Conceptual world
Modeling Simulation
Traditional Graphics
Conceptual world
Modeling Simulation
Traditional Graphics
shape models reflection models motion models
Conceptual world
Modeling Simulation
Traditional Graphics
Conceptual world
Modeling Simulation
Traditional Graphics
Pros:
+ Compact representation
+ Easy to manipulate
Cons:
- Very hard to build realistic models
- Too complex to simulate
Data-driven Graphics
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
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: [email protected]
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?