2009 年 電子情報通信学会総合大会

A-2-25

Expression-Driven Sketch Animation
Yang Yang*1,*2Yuehu Liu*1*1 Institute of Artificial Intelligence and Robotics,
Xi’an Jiaotong University, Xi’an, China

Yoshifumi Nishio*2 Yu Shao*1*2 Electrical and Electronic Engineering,
The University of Tokushima, Tokushima, Japan

Abstract

suitable

An automatic sketch animation method is presented for cloning
facial expressions. Different from the prior approaches, we try to
animate a sketch with the set of real facial expression samples.
When transfer the source expression to the target face, the local
deformation characters for each target organ are learned from thesamples, which preserve the personality as well as the expression
style. The experiments show that generated sketch expression isnot only expressive but also close to reality.

component c, c ∈ (1, K , 7) , which match the T ′ .

1. Introduction
Facial sketch is a highly abstract representation of the originalface. Animating the sketch directly could avoid the complex
changes caused by illumination and texture of the photorealistic
image, but still maintain the representing power of displaying thefacial personality and emotions.
The goal is to produce sketch expression animations by clone
expressions of the source face. The source expression intensity
and act variations should be transferred, while the personality ofthe target face should be preserved in the animating results. The
proposed approach is first transferring the expressions from thesource, and then learning the local deformation constraints fromface samples to refine the final results.

deformation

function

forlocale2min( T ′ − fi (T ) + λ T − Sic
ecc
Nc
Nc 2i(1))

Where, i ∈ (1, K , n) is the sample index in the same clusterwith target organ. λ is the weight. TN and S i are the target
cc

neutral organ and a sample neutral organ respectively. There is awide choice for deformation function f . The nonlinear
deformation Thin-plate splines[1] is utilized at here to find theindex i of samples, then to deform the shape of neutral target
face locally by fi .

4. Experiment results
We selected 70 persons from Cohn-Kanade Facial Expression
Database[2] as the samples. For each person one neutral and one
surprise images were used. The target face could be any person’ssketch not limited in the database. Figure 1 shows the results ofour method. As the intensity of source expression changes, theexpression motion could be transferred to the target but still look
like the original sketch.

2. Expression Transfer
Given a facial shape, the corresponding sketch results can be
drawn by warping the sketch rendering vectors. A set of featurepoints is used to describe the face shape and the coordinate
vectors are the face parameters. We also choose some marker
points which do not change during the expressions such as eye
corners and nose tip for aligning the source and target faces.
Human’s faces share the same topology structure. Cloning
expression is to map the expression motion from the source tothe target. First, the motion vector of the source face istransferred onto the target face directly. The transfer result isdenoted as Te , which most preserve the source expression
motions. It is easy to generate invalid face without adjusting themagnitude or direction of motion vector. Te is then reconstructedin the specific expression space by principle components toacquire a valid face Te′ .

Fig.1. Expression-driven sketch animation, the upper row is sourceexpressions, and the bottom row is the generated sketch animating.

5. Conclusions

Since the global facial expression structure could beguaranteed in Te′ , the local deformation relations is learned fortarget face from the samples lately, which will animate the target
face to produce the most similar face as Te′ .

In expression cloning, both of the characters of source expression
and target personality should be preserved. An intuitive method
is presented for animating sketch face following the source
expression with constraints of local deformation. The experimentresults showed the effect of proposed method.
References

3. Local deformation constraint[1]F.L.Bookstein, “Principal

Similar shapes of the neutral face could be assumed share thesame deformation function when they do the same expressions.
K-means clustering is employed for neutral samples’ localorgans: two eyes, two eyebrows, nose, mouth and contour. Eachtarget organ chooses the deformation functions in the samecluster as candidates. Optimization function is built to finddecomposition of deformations,” IEEE Transactions on Pattern Analysis

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松山市

warps: Thin-plate splines and theand Machine Intelligence, vol.11, pp. 567-585, 1989.
[2] T.Kanade, J.F.Cohn and Y.Tian, “Comprehensive database for facial
expression analysis,” Automatic Face and Gesture Recognition (FG’00),
pp. 46-53, 2000.

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