Abstract
Computer-generated speech animations are commonly seen in video games and movies. Although high-quality facial motions can be created by the hand crafted work of skilled artists, this approach is not always suitable because of time and cost constraints. A data-driven approach [Taylor et al. 2012], such as machine learning to concatenate video portions of speech training data, has been utilized to generate natural speech animation, while a large number of target shapes are often required for synthesis. We can obtain smooth mouth motions from prepared lip shapes for typical vowels by using an interpolation of lip shapes with Gaussian mixture models (GMMs) [Yano et al. 2007]. However, the resulting animation is not directly generated from the measured lip motions of someone's actual speech.
| Original language | English |
|---|---|
| Title of host publication | ACM SIGGRAPH 2013 Posters, SIGGRAPH 2013 |
| DOIs | |
| Publication status | Published - 2013 |
| Event | ACM Special Interest Group on Computer Graphics and Interactive Techniques Conference, SIGGRAPH 2013 - Anaheim, CA Duration: 2013 Jul 21 → 2013 Jul 25 |
Other
| Other | ACM Special Interest Group on Computer Graphics and Interactive Techniques Conference, SIGGRAPH 2013 |
|---|---|
| City | Anaheim, CA |
| Period | 13/7/21 → 13/7/25 |
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ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Computer Vision and Pattern Recognition
- Software
Cite this
Efficient speech animation synthesis with vocalic lip shapes. / Mima, Daisuke; Maejima, Akinobu; Morishima, Shigeo.
ACM SIGGRAPH 2013 Posters, SIGGRAPH 2013. 2013. 2.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Efficient speech animation synthesis with vocalic lip shapes
AU - Mima, Daisuke
AU - Maejima, Akinobu
AU - Morishima, Shigeo
PY - 2013
Y1 - 2013
N2 - Computer-generated speech animations are commonly seen in video games and movies. Although high-quality facial motions can be created by the hand crafted work of skilled artists, this approach is not always suitable because of time and cost constraints. A data-driven approach [Taylor et al. 2012], such as machine learning to concatenate video portions of speech training data, has been utilized to generate natural speech animation, while a large number of target shapes are often required for synthesis. We can obtain smooth mouth motions from prepared lip shapes for typical vowels by using an interpolation of lip shapes with Gaussian mixture models (GMMs) [Yano et al. 2007]. However, the resulting animation is not directly generated from the measured lip motions of someone's actual speech.
AB - Computer-generated speech animations are commonly seen in video games and movies. Although high-quality facial motions can be created by the hand crafted work of skilled artists, this approach is not always suitable because of time and cost constraints. A data-driven approach [Taylor et al. 2012], such as machine learning to concatenate video portions of speech training data, has been utilized to generate natural speech animation, while a large number of target shapes are often required for synthesis. We can obtain smooth mouth motions from prepared lip shapes for typical vowels by using an interpolation of lip shapes with Gaussian mixture models (GMMs) [Yano et al. 2007]. However, the resulting animation is not directly generated from the measured lip motions of someone's actual speech.
UR - http://www.scopus.com/inward/record.url?scp=84881589143&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84881589143&partnerID=8YFLogxK
U2 - 10.1145/2503385.2503388
DO - 10.1145/2503385.2503388
M3 - Conference contribution
AN - SCOPUS:84881589143
SN - 9781450323420
BT - ACM SIGGRAPH 2013 Posters, SIGGRAPH 2013
ER -