摘要
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
Today massive collections of data can be ob- tained across different sources （or domains）, e.g., the depth data from Kinect, the geometrical data from scanning devices, the imagery/video data from cam- eras, and the motion data from mocap devices. Since heterogeneous data may have different discriminative powers and are intrinsically complementary for cer- tain tasks, it is desirable to leverage all the informa- tion 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.
