Hybrid learning-based model for exaggeration style of facial caricature

Prediction of facial caricature based on exaggeration style of a particular artist is a significant task in computer generated caricature in order to produce an artistic facial caricature that is very similar to the real artist’s work without the need for skilled user (artist) input. The exaggeration style of an artist is difficult to be coded in algorithmic method. Fortunately, artificial neural network, which possesses self-learning and generalization ability, has shown great promise in addressing the problem of capturing and learning an artist’s style to predict a facial caricature. However, one of the main issues faced by this study is inconsistent artist style due to human factors and limited collection on image-caricature pair data. Thus, this study proposes facial caricature dataset preparation process to get good quality dataset which captures the artist’s exaggeration style and a hybrid model to generalize the inconsistent style so that a better, more accurate prediction can be obtained even using small amount of dataset. The proposed data preparation process involves facial features parameter extraction based on landmark-based geometric morphometric and modified data normalization method based on Procrustes superimposition method. The proposed hybrid model (BP-GANN) combines Backpropagation Neural Network (BPNN) and Genetic Algorithm Neural Network (GANN). The experimental result shows that the proposed hybrid BP-GANN model is outperform the traditional hybrid GA-BPNN model, individual BPNN model and individual GANN model. The modified Procrustes superimposition method also produces a better quality dataset than the original one

Modeling Caricature Expressions by 3D Blendshape and Dynamic Texture

The problem of deforming an artist-drawn caricature according to a given normal face expression is of interest in applications such as social media, animation and entertainment. This paper presents a solution to the problem, with an emphasis on enhancing the ability to create desired expressions and meanwhile preserve the identity exaggeration style of the caricature, which imposes challenges due to the complicated nature of caricatures. The key of our solution is a novel method to model caricature expression, which extends traditional 3DMM representation to caricature domain. The method consists of shape modelling and texture generation for caricatures. Geometric optimization is developed to create identity-preserving blendshapes for reconstructing accurate and stable geometric shape, and a conditional generative adversarial network (cGAN) is designed for generating dynamic textures under target expressions. The combination of both shape and texture components makes the non-trivial expressions of a caricature be effectively defined by the extension of the popular 3DMM representation and a caricature can thus be flexibly deformed into arbitrary expressions with good results visually in both shape and color spaces. The experiments demonstrate the effectiveness of the proposed method.Comment: Accepted by the 28th ACM International Conference on Multimedia (ACM MM 2020

A review of facial caricature generator

Caricature is a pictorial description of a person or subject in a summarizing way using exaggeration of the most distinguish features and oversimplification of the common features in order to make that subject ‘unique’ and to preserve the recognizable likeness of the subject. Facial caricature generator is developed to assist the user in producing facial caricature automatically or semi-automatically. It is derived from the rapid advance in computer graphics and computer vision as well as introduced as a part of non-photorealistic rendering technologies. Recently, facial caricature generator becomes particularly interesting research topic due to the advantageous features of privacy, security, simplification, amusement and their rampant emergent realworld application such as in magazine, digital entertainment, Internet and mobile application. This paper reviews the uses of caricature in variety of applications, theories and rules in the art of drawing caricature, how these theories are simulated in the development of caricature generation system and the current research trend in this field. There are two main categories of facial caricature generator based on their input data type: human centered approach and image centered approach. It also briefly explains the general process of generating caricature. The state of the art techniques in generating caricature are described in detail by classifying it into four approaches: interactive, regularity-based, learning-based and predefined database of caricature illustration. Expressive caricature is also introduced which is evolved from the neutral caricature. This paper also discusses relevant issues, problems and several promising directions for future research

Brain-inspired computing for solving inverse kinematics of 3D human walking

Character animation using virtual human is usually used in many application areas of computing such as virtual reality, 3D games, simulation and animation. A common method of animation is to move objects by placing its in different positions and interpolating. Many character animations are tedious process due to trial and error procedure for 3D positioning and orientation of objects. Positioning can be done manually in which the user needs to specify the angles of each joint of the figure at a time or it can be done using kinematics techniques. Most of animators use inverse kinematics algorithm to control the posture of a 3D character, the root and the lengths of the links