标题: DeepSketch2Face: A Deep Learning Based Sketching System for 3D Face and Caricature Modeling
Abstract: Face modeling has been paid much attention in the field of visual computing. There exist many scenarios, including cartoon characters, avatars for social media, 3D face caricatures as well as face-related art and design, where low-cost interactive face modeling is a popular approach especially among amateur users. In this talk, I will introduce a deep learning based sketching system for 3D face and caricature modeling. The system has a labor-efficient sketching interface, that allows the user to draw freehand imprecise yet expressive 2D lines representing the contours of facial features. A novel CNN based deep regression network is designed for inferring 3D face models from 2D sketches. The proposed system also supports gesture based interactions for users to further manipulate initial face models. Both user studies and numerical
results indicate that our sketching system can help users create face models quickly and effectively. A significantly expanded face database with diverse identities, expressions and levels of exaggeration is constructed to promote further research and evaluation of face modeling techniques.
Bio: Xiaoguang Han is currently a final-year Ph.D. student with the Department of Computer Science at the University of Hong Kong since 2013. He received his M.Sc. in Applied Mathematics (2011) from Zhejiang University, and his B.S. in Information and Computer Science (2009) from Nanjing University of Aeronautics and Astronautics, China. He was also a Research Associate of School of Creative Media at City University of Hong Kong during 2011 to 2013. His research interests
include Computer Graphics, Computer Vision and Computational Geometry, especially on image/video editing, 3D reconstruction, discrete geodesic computing. His current research focuses on high-quality 3D modeling and reconstruction using deep neural networks.