Fully automatic facial deformation transfer

Authors: Bian, S., Zheng, A., Gao, L., Maguire, G., Kokke, W., Macey, J., You, L. and Zhang, J.

http://eprints.bournemouth.ac.uk/33886/

Journal: Symmetry

Publisher: MDPI AG

ISSN: 2073-8994

DOI: 10.3390/sym12010027

This data was imported from Scopus:

Authors: Bian, S., Zheng, A., Gao, L., Maguire, G., Kokke, W., Macey, J., You, L. and Zhang, J.J.

http://eprints.bournemouth.ac.uk/33886/

Journal: Symmetry

Volume: 12

Issue: 1

eISSN: 2073-8994

DOI: 10.3390/SYM12010027

© 2019 by the authors. Facial Animation is a serious and ongoing challenge for the Computer Graphic industry. Because diverse and complex emotions need to be expressed by different facial deformation and animation, copying facial deformations from existing character to another is widely needed in both industry and academia, to reduce time-consuming and repetitive manual work of modeling to create the 3D shape sequences for every new character. But transfer of realistic facial animations between two 3D models is limited and inconvenient for general use. Modern deformation transfer methods require correspondences mapping, in most cases, which are tedious to get. In this paper, we present a fast and automatic approach to transfer the deformations of the facial mesh models by obtaining the 3D point-wise correspondences in the automatic manner. The key idea is that we could estimate the correspondences with different facial meshes using the robust facial landmark detection method by projecting the 3D model to the 2D image. Experiments show that without any manual labelling efforts, our method detects reliable correspondences faster and simpler compared with the state-of-the-art automatic deformation transfer method on the facial models.

This data was imported from Web of Science (Lite):

Authors: Bian, S., Zheng, A., Gao, L., Maguire, G., Kokke, W., Macey, J., You, L. and Zhang, J.J.

http://eprints.bournemouth.ac.uk/33886/

Journal: SYMMETRY-BASEL

Volume: 12

Issue: 1

eISSN: 2073-8994

DOI: 10.3390/sym12010027

The data on this page was last updated at 05:24 on October 24, 2020.