Characteristic Facial Retargeting

Facial motion retargeting has been developed
mainly in the direction of representing high
fidelity between a source and a target model.
We present a novel facial motion retargeting
method that properly regards the significant
characteristics of target face model. We focus
on stylistic facial shapes and timings that
reveal the individuality of the target model
well, after the retargeting process is finished.
The method works with a range of expression
pairs between the source and the target facial
expressions and emotional sequence pairs of
the source and the target facial motions. We
first construct a prediction model to place
semantically corresponding facial shapes. Our
hybrid retargeting model, which combines
the Radial Basis Function (RBF) and Kernel
Canonical Correlation Analysis (kCCA) based
regression methods copes well with new input
source motions without visual artifacts. 1D
Laplacian motion warping follows after the
shape retargeting process, replacing stylistically
important emotional sequences and thus, representing
the characteristics of the target face.

Category:

Science & Technology

Tags:

• Face
• Retargeting
• Facial Retargeting
• VML
• KAIST
• KAIST VML
• Visual Media Lab
• Facial Animation
• Cartoon Animation