Shape Stylized Face Caricatures

摘要

Facial caricatures exaggerate key features to emphasize unique structural and personality traits. It is quite a challenge to retain the identity of the original person despite the exaggerations. We find that primitive shapes are well known for representing certain personality traits, in art and psychology literature. Unfortunately, current automated caricature generation techniques ignore the role of primitive shapes in stylization. These methods are limited to emphasizing key distances from a fixed Golden Ratio, or computing the best mapping in a proprietary example set of (real-image, cartoon portrait) pairs. We propose a novel stylization algorithm that allows expressive vector control with primitive shapes. We propose three shape-inspired ideas for caricature generation from input frontal face portraits: 1) Extrapolation in the Golden Ratio and Primitive Shape Spaces; 2) Use of art and psychology stereotype rules; 3) Constrained adaptation to a supplied cartoon mask. We adopt a recent mesh-less parametric image warp algorithm for the hair, face and facial features (eyes, mouth, eyebrows, nose, and ears) that provides fast results. The user can synthesize a range of caricatures by changing the number of identity constraints, relaxing shape change constraints, and controlling a global exaggeration scaling factor. Different cartoon templates and art rules can make the person's caricature mimic different personalities, and yet retain basic identity. The proposed method is easy to use and implement, and can be extended to create animated facial caricatures for games, film and interactive media applications.