Acromegaly, Mr Punch and caricature.

The origin of Mr Punch from the Italian Pulcinella of the Commedia dell'arte is well known but his feature, large hooked nose, protruding chin, kyphosis and sternal protrusion all in an exaggerated form also suggest the caricature of an acromegalic. This paper looks at the physical characteristics of acromegaly, the origin of Mr Punch and the development of caricature linking them together in the acromegalic caricature that now has a life of its own

The Efflorescence of Caricature by Todd Porterfield (ed.)

'International, intergenerational, and interdisciplinary' (p. xv) is how Porterfield positions this ambitious collection which analyses caricature between 1759 and 1838. A product of a conference of the same name, the essays it contains fulfil this remit admirably whilst attempting to explain the rise of caricature. Moreover, as Porterfield writes in his introductory offering, these essays seek to loosen the study of caricature from the orthodoxies of satirical print scholarship, and one suspects from the canonical texts of the field familiar to scholars of the long 18th century. This is not to say that those canons are rejected, rather The Efflorescence of Caricature foregrounds the vibrancy and variety of current research in this area, not least by moving away from the anglocentric narratives of anglophonic scholarship and the assumptions they contain. Thus in this desire to ask new questions of this source material alone, the collection represents a far from insignificant success...

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

Weakly-supervised Caricature Face Parsing through Domain Adaptation

A caricature is an artistic form of a person's picture in which certain striking characteristics are abstracted or exaggerated in order to create a humor or sarcasm effect. For numerous caricature related applications such as attribute recognition and caricature editing, face parsing is an essential pre-processing step that provides a complete facial structure understanding. However, current state-of-the-art face parsing methods require large amounts of labeled data on the pixel-level and such process for caricature is tedious and labor-intensive. For real photos, there are numerous labeled datasets for face parsing. Thus, we formulate caricature face parsing as a domain adaptation problem, where real photos play the role of the source domain, adapting to the target caricatures. Specifically, we first leverage a spatial transformer based network to enable shape domain shifts. A feed-forward style transfer network is then utilized to capture texture-level domain gaps. With these two steps, we synthesize face caricatures from real photos, and thus we can use parsing ground truths of the original photos to learn the parsing model. Experimental results on the synthetic and real caricatures demonstrate the effectiveness of the proposed domain adaptation algorithm. Code is available at: https://github.com/ZJULearning/CariFaceParsing .Comment: Accepted in ICIP 2019, code and model are available at https://github.com/ZJULearning/CariFaceParsin

Caricature Synthesis Based on Mean Value Coordinates

In this paper, a novel method for caricature synthesis is developed based on mean value coordinates (MVC). Our method can be applied to any single frontal face image to learn a specified caricature face exemplar pair for frontal and side view caricature synthesis. The technique only requires one or a small number of caricature face pairs and a natural frontal face 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 extended to facial expression transfer, interpolation and exaggeration, which are applications of expression editing. Moreover, the deformation equation of MVC is modified to handle the case of polygon intersections and applied to lateral view caricature synthesis from a single frontal view image. Using experiments we demonstrate that the transferred expressions are credible and the resulting caricatures can be characterized and recognized