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Front. Inform. Technol. Electron. Eng.  2013, Vol. 14 Issue (7): 475-476    DOI: 10.1631/jzus.CIDE1300
    
Data-driven digital entertainment: a computational perspective
Yue-ting Zhuang
Zhejiang University, Hangzhou 310027, China
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Abstract  Today massive collections of data can be obtained across different sources (or domains), e.g., the depth data from Kinect, the geometrical data from scanning devices, the imagery/video data from cameras, and the motion data from mocap devices. Since heterogeneous data may have different discriminative powers and are intrinsically complementary for certain tasks, it is desirable to leverage all the information available in digital entertainment. For example, the acquired 3D geometry and texture are jointly exploited to construct the colored 3D environment models; high-resolution geometry and motion-captured data are obtained to synthesize and re-target facial animations; both visual and acoustical features are contextually applied to classification. Therefore, it poses a significant challenge for the appropriate utilization of the different varieties of heterogeneous data in digital entertainment.

Published: 01 July 2013
Cite this article:

Yue-ting Zhuang. Data-driven digital entertainment: a computational perspective. Front. Inform. Technol. Electron. Eng., 2013, 14(7): 475-476.

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http://www.zjujournals.com/xueshu/fitee/10.1631/jzus.CIDE1300     OR     http://www.zjujournals.com/xueshu/fitee/Y2013/V14/I7/475


Data-driven digital entertainment: a computational perspective

Today massive collections of data can be obtained across different sources (or domains), e.g., the depth data from Kinect, the geometrical data from scanning devices, the imagery/video data from cameras, and the motion data from mocap devices. Since heterogeneous data may have different discriminative powers and are intrinsically complementary for certain tasks, it is desirable to leverage all the information available in digital entertainment. For example, the acquired 3D geometry and texture are jointly exploited to construct the colored 3D environment models; high-resolution geometry and motion-captured data are obtained to synthesize and re-target facial animations; both visual and acoustical features are contextually applied to classification. Therefore, it poses a significant challenge for the appropriate utilization of the different varieties of heterogeneous data in digital entertainment.
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