Efficient Modeling of An Anatomy-Based Face and Fast 3D Facial Expression Synthesis

This paper presents new methods for efficient modeling and animation of an hierarchical facial model that conforms to the human face anatomy for realistic and fast 3D facial expression synthesis. The facial model has a skin–muscle–skull structure. The deformable skin model directly simulates the nonlinear visco‐elastic behavior of soft tissue and effectively prevents model collapse. The construction of facial muscles is achieved by using an efficient muscle mapping approach. Based on a cylindrical projection of the texture‐mapped facial surface and wire‐frame skin and skull meshes, this approach ensures different muscles to be located at the anatomically correct positions between the skin and skull layers. For computational efficiency, we devise an adaptive simulation algorithm which uses either a semi‐implicit integration scheme or a quasi‐static solver to compute the relaxation by traversing the designed data structures in a breadth‐first order. The algorithm runs in real‐time and has successfully synthesized realistic facial expressions. ACM CSS: I.3.5 Computer Graphics: Computational Geometry and Object Modelling—physically based modelling; I.3.7 Computer Graphics: Three‐Dimensional Graphics and Realism—animation;     

Computer‐Suggested Facial Makeup

Finding the best makeup for a given human face is an art in its own right. Experienced makeup artists train for years to be skilled enough to propose a best‐fit makeup for an individual. In this work we propose a system that automates this task. We acquired the appearance of 56 human faces, both without and with professional makeup. To this end, we use a controlled‐light setup, which allows to capture detailed facial appearance information, such as diffuse reflectance, normals, subsurface‐scattering, specularity, or glossiness. A 3D morphable face model is used to obtain 3D positional information and to register all faces into a common parameterization. We then define makeup to be the change of facial appearance and use the acquired database to find a mapping from the space of human facial appearance to makeup. Our main application is to use this mapping to suggest the best‐fit makeup for novel faces that are not in the database. Further applications are makeup transfer, automatic rating of makeup, makeup‐training, or makeup‐exaggeration. As our makeup representation captures a change in reflectance and scattering, it allows us to synthesize faces with makeup in novel 3D views and novel lighting with high realism. The effectiveness of our approach is further validated in a user‐study.     

Performance‐driven animation of hand‐drawn cartoon faces

We present a novel performance‐driven approach to animating cartoon faces starting from pure 2D drawings. A 3D approximate facial model automatically built from front and side view master frames of character drawings is introduced to enable the animated cartoon faces to be viewed from angles different from that in the input video. The expressive mappings are built by artificial neural network (ANN) trained from the examples of the real face in the video and the cartoon facial drawings in the facial expression graph for a specific character. The learned mapping model makes the resultant facial animation to properly get the desired expressiveness, instead of a mere reproduction of the facial actions in the input video sequence. Furthermore, the lit sphere, capturing the lighting in the painting artwork of faces, is utilized to color the cartoon faces in terms of the 3D approximate facial model, reinforcing the hand‐drawn appearance of the resulting facial animation. We made a series of comparative experiments to test the effectiveness of our method by recreating the facial expression in the commercial animation. The comparison results clearly demonstrate the superiority of our method not only in generating high quality cartoon‐style facial expressions, but also in speeding up the animation production of cartoon faces. Copyright © 2011 John Wiley & Sons, Ltd.