2008 年 電子情報通信学会総合大会

A-2-8

Face Animation Framework for Facial Expression Synthesis
Yang Yang*1,*2

Yuehu Liu*1*1 Institute of Artificial Intelligence and Robotics,
Xi’an Jiaotong University, Xi’an, China

Yoshifumi Nishio*2*2 Electrical and Electronic Engineering,
The University of Tokushima, Tokushima, JapanAbstract
A face animation framework is presented for synthesizing
facial expressions, which provides a method for cloning real face
expressions to generate new facial animation sequence. Firstly,
the sample frames are re-sequenced to form a new animation
sequence, and then the target face is animated using shapeblending method.
Fig.1. Local mouth animation by shape-blending.

1. Introduction
Many researchers have tried a lot of efforts to drive a virtualface performing as human beings. We considered that automatic
facial animation system could not only map the expression ineach frame, but also learn the behaviors from sample expressions,when there is no direct sample sequence between two kinds ofexpressions, the system could search a new path for animation,
we propose a novel framework for facial animation generation,
which re-sequences the sample frames for new animations.

2. Expression Sequence
It is hard to perform all the expression sequences about how tochange from one expression to the others for samples. Our
purpose is to find a new expression sequence from the sample
frames and generate a new expression animation sequence, when
two frames are selected as the start and end expressions atrandom. Proposed solution is, firstly, reduce the dimensionalityof the expression features, and then search a new expression pathin the low-dimensional space.
The Isomap [1] can acquire the nonlinear degrees of freedom
and discover the intrinsic global structure of a data set. We use
the Isomap to embed the facial expression features into a lowdimensional space (Fig.2), the geodesic distance replaces theEuclidean distance to judge the similarity of the expressions.
Similar-looking expressions have been located closely, whichkeeps the intrinsic relations among the expressions.
To form an expression sequence, the shortest cost path issearched in the low-dimensional space by Dijkstra algorithm. Inbetween expressions are the indexes along the path after resequence the sample frames.

3. Animation Based on Shape Blending
For each frame in the new expression path, the animated result
is acquired by blending the three closest key frames together [2].
A set of extreme expressions of the target face is located in thelow-dimensional space at the corresponding expression’s
positions, and blending weight vector is related with its position.
Fig.1 shows a local mouth blending example, we judge theEuclidean distance between the point and key frame, when thedistance is closer, and the weight must bigger, vice versa. The
weight equals to 1 when the distance is zero.

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北九州市4. Experiments
Figure 2 shows our experimental results. The feature points
are extracted from a real person’s various expression images andembed into 2D space. A set of cartoon face’s key expression
images such as laughing, surprising, are located at thecorresponding positions in advance (stars in Fig.2). Chosen any
two points as the start and end expressions, the proposed
framework can search a new animation path between them (linein Fig.2). Animation results by shape blending are shown along
this new expression sequence.

Fig.2. Experimental results.

5. Conclusions
A framework for facial animation sequence is presented, it isused to generate various cartoon face expression sequence. The
experiments demonstrate that proposed method is efficient anddesirable.
References[1] J. B. Tenenbaum, V. Silva and J. C. Langford, A Global Geometric
Framework for Nonlinear Dimensionality Reduction, Science, vol. 290,no. 5500, pp. 2319-2323, 2000.
[2] I. Buck, A. Finkelstein, et., Performance-driven hand-drawn animation, In
Proceedings of Non-Photorealistic Animation and Rendering. ACM Press, pp101-108, 2000.

47（基礎・境界講演論文集）

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