Alive Caricature from 2D to 3D

Caricature is an art form that expresses subjects in abstract, simple and exaggerated view. While many caricatures are 2D images, this paper presents an algorithm for creating expressive 3D caricatures from 2D caricature images with a minimum of user interaction. The key idea of our approach is to introduce an intrinsic deformation representation that has a capacity of extrapolation enabling us to create a deformation space from standard face dataset, which maintains face constraints and meanwhile is sufficiently large for producing exaggerated face models. Built upon the proposed deformation representation, an optimization model is formulated to find the 3D caricature that captures the style of the 2D caricature image automatically. The experiments show that our approach has better capability in expressing caricatures than those fitting approaches directly using classical parametric face models such as 3DMM and FaceWareHouse. Moreover, our approach is based on standard face datasets and avoids constructing complicated 3D caricature training set, which provides great flexibility in real applications.Comment: Accepted to CVPR 201

Analysis and comparison of facial animation algorithms: caricatures

The thesis will be aimed to review what has been done from around the 2000 until now regarding the caricature generation field in 2D. It will be organized in classifying the methods found first, telling their contributions to the field and choosing a paper among them to implement and discuss more thoroughly. A total of three papers will be selected. Finally, an overview discussion on the papers implemented and their contributions to the field will be given. Brief comment on the Master Thesis small change of title: In the very beginning, when I was planning to do the thesis, I talked with my tutor and found that doing a review and comparison of some methods in the facial animation field would suit. However, while reading papers on the topic, I found that a great number of them required hardware which I didn’t have any access to. The generation of 2D caricatures is still close to the field, and it didn’t need any additional hardware devic

A Leopard Cannot Change Its Spots: Improving Face Recognition Using 3D-based Caricatures

Caricatures refer to a representation of aperson in which the distinctive features are deliberatelyexaggerated, with several studies showing that humansperform better at recognizing people from caricaturesthan using original images. Inspired by this observa-tion, this paper introduces the first fully automatedcaricature-based face recognition approach capable ofworking with data acquired in the wild. Our approachleverages the 3D face structure from a single 2D imageand compares it to a reference model for obtaininga compact representation of face features deviations.This descriptor is subsequently deformed using a ’mea-sure locally, weight globally’ strategy to resemble thecaricature drawing process. The deformed deviationsare incorporated in the 3D model using the Laplacianmesh deformation algorithm, and the 2D face cari-cature image is obtained by projecting the deformedmodel in the original camera-view. To demonstratethe advantages of caricature-based face recognition, wetrain the VGG-Face network from scratch using eitheroriginal face images (baseline) or caricatured images,and use these models for extracting face descriptorsfrom the LFW, IJB-A and MegaFace datasets. The ex-periments show an increase in the recognition accuracywhen using caricatures rather than original images.Moreover, our approach achieves competitive resultswith state-of-the-art face recognition methods, evenwithout explicitly tuning the network for any of theevaluation sets.info:eu-repo/semantics/publishedVersio

Alive caricature from 2D to 3D

Caricature is an art form that expresses subjects in abstract, simple and exaggerated views. While many caricatures are 2D images, this paper presents an algorithm for creating expressive 3D caricatures from 2D caricature images with minimum user interaction. The key idea of our approach is to introduce an intrinsic deformation representation that has the capability of extrapolation, enabling us to create a deformation space from standard face datasets, which maintains face constraints and meanwhile is sufficiently large for producing exaggerated face models. Built upon the proposed deformation representation, an optimization model is formulated to find the 3D caricature that captures the style of the 2D caricature image automatically. The experiments show that our approach has better capability in expressing caricatures than those fitting approaches directly using classical parametric face models such as 3DMM and FaceWareHouse. Moreover, our approach is based on standard face datasets and avoids constructing complicated 3D caricature training sets, which provides great flexibility in real applications

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