Download - Feature-Based Image Metamorphosis
Feature-Based Image Metamorphosis
Thaddeus Beier Shawn NeelySIGGRAPH 1992
Image Morphing History
Morphing is turning one image into another through a seamless transition
Michael Jackson’s “Black or White”Cross-fading
morphing
cross-fadingImage morphing
image #1 image #2
warp warp
Image morphing
Morphing = warping + cross-dissolving
shape(geometric)
color(photometric)
Warp = feature specification + warp generation
Warp specification
• How can we specify the warp?3. Specify corresponding spline control points
• interpolate to a complete warping function
But we want to specify only a few points, not a grid
Warp specification
• How can we specify the warp?1. Specify corresponding points
• interpolate to a complete warping function• How do we do it?
Warp specification
• How can we specify the warp?2. Specify corresponding vectors
• interpolate to a complete warping function• The Beier & Neely Algorithm
Two basic styles
• Forward warping• Reverse mapping
Single line-pair PQ to P’Q’
Single Line-pair Examples
Multiple Lines
Length = length of the line segment, dist = distance to line segmenta, p, b – constants. What do they do?
Resulting warp (complex!)
Full Algorithm
Animation• Here's how you create an animated morph:• GenerateAnimation(Image0, L0[...],Image1, L1[...])• begin• foreach intermediate frame time t do• for i=1 to number of line-pairs do• L[i] = line t-th of the way from L0[i] to L1[i].• end• Warp0 = WarpImage( Image0, L0[...], L[...])• Warp1 = WarpImage( Image1, L1[...], L[...])• foreach pixel p in FinalImage do• FinalImage(p) = (1-t) Warp0(p) + t Warp1(p)• end• end• end
Interpolating Lines
• Method 1: interpolating endpoints• Method 2: interpolating midpoints, length
and orientation.
Results
Dynamic Scene
Algorithm summary
Morphing & matting• Extract foreground first to avoid artifacts in
the background
Uniform morphing
Non-uniform morphing
Procedural Transformation
Multi-source morphing
Manipulating Facial Appearance through Shape
and ColorDuncan A. Rowland and David I. P
errettSt Andrews University
IEEE CG&A, September 1995
The Morphable Face Model
• shape vector S = (x1, y1, x2, …, yn)T
• appearance (texture) vector T = (R1, G1, B1, R2, …, Gn, Bn)T
Shape S
Appearance T
The Morphable face model• Assuming that we have m such vector pairs in full correspondence, we
can form new shapes Smodel and new appearances Tmodel as:
• If number of basis faces m is large enough to span the face subspace then:
• Any new face can be represented as a pair of vectors
m
iiimodel a
1
SS
m
iiimodel b
1
TT
Face averaging by morphing
average faceshttp://www.beautycheck.de
Subpopulation means
• Examples:– Happy faces– Young faces– Asian faces– Etc.– Sunny days– Rainy days– Etc.– Etc.
Average male
Average female
The average face
Women In Arts
http://www.youtube.com/watch?v=nUDIoN-_Hxs
References• Thaddeus Beier, Shawn Neely, Feature-Based Image Metamorphosis,
SIGGRAPH 1992, pp35-42. • Detlef Ruprecht, Heinrich Muller, Image Warping with Scattered Data
Interpolation, IEEE Computer Graphics and Applications, March 1995, pp37-43.
• Seung-Yong Lee, Kyung-Yong Chwa, Sung Yong Shin, Image Metamorphosis Using Snakes and Free-Form Deformations, SIGGRAPH 1995.
• Seungyong Lee, Wolberg, G., Sung Yong Shin, Polymorph: morphing among multiple images, IEEE Computer Graphics and Applications, Vol. 18, No. 1, 1998, pp58-71.
• Peinsheng Gao, Thomas Sederberg, A work minimization approach to image morphing, The Visual Computer, 1998, pp390-400.
• George Wolberg, Image morphing: a survey, The Visual Computer, 1998, pp360-372.
Overview of Morphing Methods– Mesh Warping– Field Morphing– Radial Basis Function– Energy minimization– Multilevel Free-Form Deformation– Work minimization
Image Morphing: A Survey George Wolberg 1998