Caricatures are a form of humorous visual art, usually created by skilled artists for the intention of amusement and entertainment. In this paper, we present a novel approach for automatic generation of digital caricatures from facial photographs, which capture artistic deformation styles from hand-drawn caricatures. We introduced a pseudo stress-strain model to encode the parameters of an artistic deformation style using “virtual” physical and material properties. We have also developed a software system for performing the caricaturistic deformation in 3D which eliminates the undesirable artifacts in 2D caricaturization. We employed a Multilevel Free-Form Deformation (MFFD) technique to optimize a 3D head model reconstructed from an input facial photograph, and for controlling the caricaturistic deformation. Our results demonstrated the effectiveness and usability of the proposed approach, which allows ordinary users to apply the captured and stored deformation styles to a variety of facial photographs.
Representing articulated objects as a graphical model has gained much popularity in recent years, often the root node of the graph describes the global position and ori- entation of the object. In this work a method is presented to robustly track 3D human pose by permitting greater un- certainty to be modeled over the root node than existing techniques allow. Significantly, this is achieved without increasing the uncertainty of remaining parts of the model. The benefit is that a greater volume of the posterior can be supported making the approach less vulnerable to tracking failure. Given a hypothesis of the root node state a novel method is presented to estimate the posterior over the remaining parts of the body conditioned on this value. All probability distributions are approximated using a single Gaussian allowing inference to be carried out in closed form. A set of deterministically selected sample points are used that allow the posterior to be updated for each part requiring just seven image likelihood evaluations making it extremely efficient. Multiple root node states are supported and propagated using standard sampling techniques. We believe this to be the first work devoted to efficient track- ing of human pose whilst modeling large uncertainty in the root node and demonstrate the presented method to be more robust to tracking failures than existing approaches.
Ben Daubney and Xianghua Xie, Tracking 3D Human Pose with Large Root Node Uncertainty, In Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2011.