Learning Face Age Progression: A Pyramid Architecture of GANs

The two underlying requirements of face age progression, i.e. aging accuracy and identity permanence, are not well studied in the literature. In this paper, we present a novel generative adversarial network based approach. It separately models the constraints for the intrinsic subject-specific characteristics and the age-specific facial changes with respect to the elapsed time, ensuring that the generated faces present desired aging effects while simultaneously keeping personalized properties stable. Further, to generate more lifelike facial details, high-level age-specific features conveyed by the synthesized face are estimated by a pyramidal adversarial discriminator at multiple scales, which simulates the aging effects in a finer manner. The proposed method is applicable to diverse face samples in the presence of variations in pose, expression, makeup, etc., and remarkably vivid aging effects are achieved. Both visual fidelity and quantitative evaluations show that the approach advances the state-of-the-art.Comment: CVPR 2018. V4 and V2 are the same, i.e. the conference version; V3 is a related but different work, which is mistakenly submitted and will be submitted as a new arXiv pape

Age Progression and Regression with Spatial Attention Modules

Age progression and regression refers to aesthetically render-ing a given face image to present effects of face aging and rejuvenation, respectively. Although numerous studies have been conducted in this topic, there are two major problems: 1) multiple models are usually trained to simulate different age mappings, and 2) the photo-realism of generated face images is heavily influenced by the variation of training images in terms of pose, illumination, and background. To address these issues, in this paper, we propose a framework based on conditional Generative Adversarial Networks (cGANs) to achieve age progression and regression simultaneously. Particularly, since face aging and rejuvenation are largely different in terms of image translation patterns, we model these two processes using two separate generators, each dedicated to one age changing process. In addition, we exploit spatial attention mechanisms to limit image modifications to regions closely related to age changes, so that images with high visual fidelity could be synthesized for in-the-wild cases. Experiments on multiple datasets demonstrate the ability of our model in synthesizing lifelike face images at desired ages with personalized features well preserved, and keeping age-irrelevant regions unchanged

Senescence: An Aging based Character Simulation Framework

The \u27Senescence\u27 framework is a character simulation plug-in for Maya that can be used for rigging and skinning muscle deformer based humanoid characters with support for aging. The framework was developed using Python, Maya Embedded Language and PyQt. The main targeted users for this framework are the Character Technical Directors, Technical Artists, Riggers and Animators from the production pipeline of Visual Effects Studios. The characters that were simulated using \u27Senescence\u27 were studied using a survey to understand how well the intended age was perceived by the audience. The results of the survey could not reject one of our null hypotheses which means that the difference in the simulated age groups of the character is not perceived well by the participants. But, there is a difference in the perception of simulated age in the character between an Animator and a Non-Animator. Therefore, the difference in the simulated character\u27s age was perceived by an untrained audience, but the audience was unable to relate it to a specific age group

A finite element model of the face including an orthotropic skin model under in vivo tension

Computer models of the human face have the potential to be used as powerful tools in surgery simulation and animation development applications. While existing models accurately represent various anatomical features of the face, the representation of the skin and soft tissues is very simplified. A computer model of the face is proposed in which the skin is represented by an orthotropic hyperelastic constitutive model. The in vivo tension inherent in skin is also represented in the model. The model was tested by simulating several facial expressions by activating appropriate orofacial and jaw muscles. Previous experiments calculated the change in orientation of the long axis of elliptical wounds on patients’ faces for wide opening of the mouth and an open-mouth smile (both 30 degrees). These results were compared with the average change of maximum principal stress direction in the skin calculated in the face model for wide opening of the mouth (18o) and an openmouth smile (25 degrees). The displacements of landmarks on the face for four facial expressions were compared with experimental measurements in the literature. The corner of the mouth in the model experienced the largest displacement for each facial expression (11–14 mm). The simulated landmark displacements were within a standard deviation of the measured displacements. Increasing the skin stiffness and skin tension generally resulted in a reduction in landmark displacements upon facial expression

Physically-based forehead animation including wrinkles

Physically-based animation techniques enable more realistic and accurate animation to be created. We present a fully physically-based approach for efficiently producing realistic-looking animations of facial movement, including animation of expressive wrinkles. This involves simulation of detailed voxel-based models using a graphics processing unit-based total Lagrangian explicit dynamic finite element solver with an anatomical muscle contraction model, and advanced boundary conditions that can model the sliding of soft tissue over the skull. The flexibility of our approach enables detailed animations of gross and fine-scale soft-tissue movement to be easily produced with different muscle structures and material parameters, for example, to animate different aged skins. Although we focus on the forehead, our approach can be used to animate any multi-layered soft body

Face

The face is probably the part of the body, which most distinguishes us as individuals. It plays a very important role in many functions, such as speech, mastication, and expression of emotion. In the face, there is a tight coupling between different complex structures, such as skin, fat, muscle, and bone. Biomechanically driven models of the face provide an opportunity to gain insight into how these different facial components interact. The benefits of this insight are manifold, including improved maxillofacial surgical planning, better understanding of speech mechanics, and more realistic facial animations. This chapter provides an overview of facial anatomy followed by a review of previous computational models of the face. These models include facial tissue constitutive relationships, facial muscle models, and finite element models. We also detail our efforts to develop novel general and subject-specific models. We present key results from simulations that highlight the realism of the face models