Mean value coordinates–based caricature and expression synthesis

We present a novel method for caricature synthesis based on mean value coordinates (MVC). Our method can be applied to any single frontal face image to learn a specified caricature face pair for frontal and 3D caricature synthesis. This technique only requires one or a small number of exemplar pairs and a natural frontal face image training set, while the system can transfer the style of the exemplar pair across individuals. Further exaggeration can be fulfilled in a controllable way. Our method is further applied to facial expression transfer, interpolation, and exaggeration, which are applications of expression editing. Additionally, we have extended our approach to 3D caricature synthesis based on the 3D version of MVC. With experiments we demonstrate that the transferred expressions are credible and the resulting caricatures can be characterized and recognized

Markov Weight Fields for face sketch synthesis

Posters 1C - Vision for Graphics, Sensors, Medical, Vision for Robotics, ApplicationsGreat progress has been made in face sketch synthesis in recent years. State-of-the-art methods commonly apply a Markov Random Fields (MRF) model to select local sketch patches from a set of training data. Such methods, however, have two major drawbacks. Firstly, the MRF model used cannot synthesize new sketch patches. Secondly, the optimization problem in solving the MRF is NP-hard. In this paper, we propose a novel Markov Weight Fields (MWF) model that is capable of synthesizing new sketch patches. We formulate our model into a convex quadratic programming (QP) problem to which the optimal solution is guaranteed. Based on the Markov property of our model, we further propose a cascade decomposition method (CDM) for solving such a large scale QP problem efficiently. Experimental results on the CUHK face sketch database and celebrity photos show that our model outperforms the common MRF model used in other state-of-the-art methods. © 2012 IEEE.published_or_final_versionThe IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI., 16-21 June 2012. In IEEE Conference on Computer Vision and Pattern Recognition Proceedings, 2012, p. 1091-109

Face Sketch Synthesis with Style Transfer using Pyramid Column Feature

1C Action / Pose / Biometric - paper no. 3

Face Sketch Synthesis with Style Transfer using Pyramid Column Feature

1C Action / Pose / Biometric - paper no. 3

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