lyu0603 a generic real-time facial expression modelling system supervisor: prof. michael r. lyu...
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LYU0603
A Generic Real-Time Facial Expression Modelling System
Supervisor:
Prof. Michael R. Lyu
Group Member:
Cheung Ka Shun (05521661)
Wong Chi Kin (05524554)
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Outline
• Previous Work• Objectives• Work in Semester Two• Review of implementation tool• Implementation
– Virtual Camera– 3D Face Generator– Face Animation
• Conclusion• Q&A
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Previous Work
• Face analysis– Detect the facial expression– Draw corresponding model
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Objectives
• Enrich the functionality of web-cam
• Make net-meeting more interesting
• Users are not required to pay extra cost on specific hardware
• Extract human face and approximate face shape
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Work in Semester Two
• Virtual Camera– Make Facial Expression Modelling to be
available in net-meeting software
• Face Generator– Approximate face shape– Generate a 3D face texture
• Face Animation– Animate the generated 3D face– Convert into standard file format
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Review - DirectShow
• Filter graph– Source Transform Renderer
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Review - Direct3D
• Efficiently process and render 3-D scenes to a display, taking advantage of available hardware
• Fully Compatible with DirectShow
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Virtual Camera
• Focus on MSN Messenger
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Virtual Camera• Two components
– 3D model as output– Face Mesh Preview
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Virtual Camera
• Actually it is a source filter
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Virtual Camera
FaceExModelingSample Grabber Null RendererFaceCoord
Capture Device
Virtual Camera
Video Renderer
Create an Inner Filter Graph
• Inner filter graph in virtual camera
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Virtual Camera
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Demonstration
• We are going to play a movie clip which demonstrate Virtual Camera
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3D Face Generator
• Aims: To approximate the human face and shape
• Comprise two parts
Face Texture Generator
FaceLab
Common Buffer
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Face Texture Generator
FaceLab
Common Buffer
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FaceLab
• Adopted from the face analysis project of Zhu Jian Ke, CUHK CSE Ph.D. Student
• The analysis is decomposed into training and building part
• The whole training phase is made up of three steps
3D Data Acquisition 3D Data Registration Shape Model Building
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• However, thousands of point are demanded to describe the complex structure of human face
• It can be acquired either by 3D scanner or computer vision algorithm
FaceLab – Data Acquisition
• To acquire human face structure data
• In 2D face modelling, 100 feature points are sufficient to represent the face surface
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FaceLab – Data Registration
• To normalize the 3D data into same scale with correspondences
• Problem– The most accurate way is to compute
3D optical flow – Commercial 3D scanners and 3D
registration are computed with specific hardware
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FaceLab
• To simplify the process, it is decided to use software to generate the human face data.
• Each has a set of 752 3D vertex data to describe the shape of face
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FaceLab – Shape Model Building
• A shape is defined as a geometry data by removing the translational, rotational and scaling components.
• The object containing N vertex data is represented as a matrix below
ZYXS
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FaceLab – Shape Model Building
• The set of P shapes will form a point cloud in 3N-dimensional space which is a huge domain.
• A conventional principle component analysis (PCA) is performed.
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FaceLab – Shape Model BuildingPCA Implementation
• It performs an orthogonal linear transform
• A new coordinate system which points to the directions of maximum variation of the point cloud.
• In this Implementation, the covariance method is used.
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FaceLab – Shape Model BuildingPCA Implementation
• Step 1: Compute the empirical mean
which is the mean shape along each dimension
P
iiSP
S1
1
S
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FaceLab – Shape Model BuildingPCA Implementation
• Step 2: Calculate the covariance matrix C
• The axes of the point cloud are collected from the eigenvectors of the covariance matrix.
Ti
P
ii SSSS
PC
1
1
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FaceLab – Shape Model BuildingPCA Implementation
• Step 3: Compute the matrix of eigenvectors V
where D is the eigenvalue matrix of C
• The eigenvalue represents the distribution of the objects data’s energy
DCVV 1
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FaceLab – Shape Model BuildingPCA Implementation
• Final Step: Represent the resulted shape model as
where ms are the shape parameters
• Adjusting the value of the shape parameters can generate a new face model by computing
ssmVSS
)( SSVm Tss
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FaceLab – Shape Model BuildingPCA Implementation
• An extra step: Select a subset of the eigenvectors
• The eigenvalue represents the variation of the corresponding axis
• The first seven columns are used in the system and achieve a majority of the total variance.
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FaceLab – Render the face model
• The resulted data set is a 3D face mesh data
• Use OpenGL to render it
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Face Texture Generator
FaceLab
Common Buffer
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System Overview of Face Texture Generator
Facial Expression Modelling Face Texture Generator
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Face Texture Generator
• Face texture extraction– Three Approaches
• Largest area triangle aggregation
• Human-defined triangles aggregation
• Single photo on effect face
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Largest area triangle aggregation
Left face Right faceFront face
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Largest area triangle aggregation
Left Face Front Face Right Face
for each triangle in face mesh
Extract the triangle with largest area
Face texture
……
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Largest area triangle aggregation
• ResultTriangles was sampled from
different photo
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Human-defined triangles aggregation
• Divide the face mesh into three parts
• Define the particular photo to be sampled in triangles in each region
• Reduce fragmentation
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Human-defined triangles aggregation
• Redefine the face mesh – Effect Face
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Human-defined triangles aggregation
• Result
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Single photo on effect face
• Similar to Human-defined triangles aggregation
• Use a single photo for pixel sampling
• Use Effect Face as outline
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Single photo on effect face
• Result
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Face Generator Filter
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Dynamic Texture Generation
• To get back the rendered data from the video display card
Face Generator FaceLab
Common Buffer
Video Card Buffer
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Dynamic Texture Generation
• Lock the video display buffer
Face Generator FaceLab
Common Buffer
Video Card Buffer
Lock and copy the buffer to the common
buffer
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Dynamic Texture Generation
Face Generator FaceLab
Common Buffer
Video Card Buffer
Update the texture with the common buffer
• Common buffer content is changed• Update the texture buffer to reflect the
changes immediately
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Dynamic Texture Generation
• From 2D face mesh to 3D face mesh
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Completed 3D Face Generator
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Demonstration
• We are going to play a movie clip which demonstrates Face Generator
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Face Viewer
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Generate simple animation
Looking at the mouse cursor
• Feature points provide sufficient information to locate the eye
• The two eyes will form a triangle planar with the mouse cursor
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Generate simple animation
Eye blinking
• One of the natural movements on a human face
• Adjust the vertex geometry on the eyelids
• The eyebrow is also needed to move backward
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Generate simple animation
• A lot of muscle needed to be modified
• The cheeks are being pushed up and wide
• The chin is being pulled down
• To change the shape of the lip
Smiling
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Convert into standard file format
• Save the mesh in .x file format
• Current selected face mesh data would be saved
• The Microsoft DirectX .x file format is not specific
• It can be used by any other application
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Demonstration
• We are going to play a movie clip which demonstrate Face Animation
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Conclusion
• We have achieved our goals– Enrich functionality of webcam– Make net-meeting become more
interesting– Extract human face and approximate face
shape– Discover the potential of face recognition
technology
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End
• Thank you!
• Q&A