DeepSketch2Face: A Deep Learning Based Sketching System

for 3D Face and Caricature Modeling


Xiaoguang Han       Chang Gao       Yizhou Yu


The University of Hong Kong


ACM Transactions on Graphics (Proceedings of SIGGRAPH 2017)




Fig. 1. Using our sketching system, an amateur user can create 3D face or caricature models with complicated shape and expression in a few minutes. Both models shown here were created in less than 10 minutes by a user without any prior drawing and modeling experiences.




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 paper, we propose a deep learning based sketching system for 3D face and caricature modeling. This 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. Our network fuses both CNN and shape based features of the input sketch, and has two independent branches of fully connected layers generating independent subsets of coefficients for a bilinear face representation. Our 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.




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Fig. 2.   Our sketching system has three interaction modes: the initial sketching mode, follow-up sketching model and gesture-based refinement mode. In the initial sketching mode, the 3D face is updated immediately after each operation. The follow-up sketching mode gets started when an output model (a) in the initial sketching model is rendered to a sketch (b). A sequence of operations in this model are shown from (b) to (h). Users can switch in real time from 2D sketching to 3D model viewing (e.g. (d) to (i), (g) to (j) and (h) to (k)). The created shape (k) can be refined in the gesture-based refinement mode. (l) and (m) show the gestures used for depth depressing and bulging, and the corresponding results after each operation are shown in (n) and (o). A red solid arrow indicates a single operation while a dashed one means several operations, and a blue arrow stands for model updating.




Network Architecture


Fig. 3.   Our network architecture.



Results Gallery


Fig. 4.   A gallery of results created using our sketching system. On average, each model was created in around 8 minutes.




The authors would like to thank the reviewers for their constructive comments, and the participants of our user study for their precious time.




        author = "Han, X. and Gao, C. and Yu, Y.",
        title = "DeepSketch2Face: A Deep Learning Based Sketching System for 3D Face and Caricature Modeling",
        journal = "{ACM} Transactions on Graphics",
        volume = "36",
        number = "4",
        pages = "",
        month = "July",
        year = "2017"



Copyright © 2017 Xiaoguang Han