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Human Computer Interaction 7. Advanced User Interfaces (I) Data Scattering and RBF

Human Computer Interaction 7. Advanced User Interfaces (I) Data Scattering and RBF. Course no. ILE5013 National Chiao Tung Univ, Taiwan By: I-Chen Lin, Assistant Professor. Introduction. Given a set of samples, what are the in-between values ?

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Human Computer Interaction 7. Advanced User Interfaces (I) Data Scattering and RBF

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  1. Human Computer Interaction7. Advanced User Interfaces (I)Data Scattering and RBF Course no. ILE5013 National Chiao Tung Univ, Taiwan By: I-Chen Lin, Assistant Professor

  2. Introduction • Given a set of samples, what are the in-between values ? • Linear interpolation, interpolating by splines, … • It works for structured data. How about unstructured or scattered data samples?

  3. Scattered Data Interpolation For instance, head model adjustment…

  4. ? What are the displacement ? ? ? Scattered Data Interpolation You can drag all vertices (more than 6000) or drag feature samples… Drag by users

  5. Scattered Samples Kover et al, “Automated Extraction and Parameterization of Motions in Large Data Sets”, Proc. SIGGRAPH’04.

  6. Interpolation • Characteristics • Interpolation vs. Extrapolation • Linear Interpolation vs. Higher Order • Structured vs. Scattered • 1-Dimensional vs. Multi-Dimensional • Techniques • Splines (cubic, B-splines, …) • Series (polynomial, radial basis functions, …) • ……… • Exact solution, minimization, fitting, approximation

  7. Scattered Data Interpolation • Given N samples (xi, fi), such that S(xi)=fi, We would like to reconstruct a function S(x). • Actually, there’re infinite solutions. • Our constraints: • S(x) should be continuous over the entire domain • We want a ‘smooth’ surface • Radial basis functions are popularly used solutions.

  8. Radial Basis Function f(x) X R.Gutierrez-Osuna, Intro to Pattern Analysis, Texas, A&M Univ.

  9. The RBF Solution Ref: http://www.unknownroad.com/rtfm/rbf_rms2002.ppt

  10. Radial Basis Functions •  can be any function • For instance, • M(x) = a + bx + cy + dz • 4 coefficients a,b,c,d • Adds 4 equations and 4 variables to the linear system

  11. Finding an RBF Solution • The weights and polynomial coefficients are unknowns • We know N values of fj: • N equations fj = S(xj) • 4 additional constraints

  12. The Linear System Ax = b

  13. Radial Basis Function f(x) X • i define the influence of the centre • After constructing S(x), the interpolation or extrapolation can be easily performed.

  14. Applications of RBF • Image/Object warping, morphing • Surface reconstruction • Range scanning, geographic surveys, medical data • Field Visualization (2D and 3D) • AI

  15. Applications of RBF in Graphics Hole filling Surface reconstruction

  16. Applications of RBF in Graphics Morphing with influence shapes Medical visualization …..

  17. Keyframing with RBF • In traditional temporal keyframing, key poses are defined at specific points in time. • How to manipulate key poses with an intuitive interface? • T.Igarashi, et al. “Spatial Keyframing for Performance-driven Animation”, Proc. SCA’05.

  18. Animation Authoring with RBF Define key poses Spatially keyframing animation

  19. Animation Authoring with RBF • Using RBF • X: the position of control cursors. • S: pose, a set of local transformations of the body parts. • Why not angular parameters • Since interpolating in 3D space not in sequential time space. • And …

  20. Animation Authoring with RBF

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