Performance-driven animation of hand-drawn cartoon faces

Abstract

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

A Simple Line Drawing Definition and Transfer Model for Facial Animation Generation

“…[7] proposed a neighbour expression transfer model (NET) to transfer expressions and [8] presents a method to get expressive caricatures of basic main expressions but they need that there is a approximate face existed in the train set. [9], create multi-view hand-drawn sketches by using a hybrid sketch-based method. [10] use a facial expression graph to extract the facial actions and then transfer that to cartoon model based on ANN.…”