A Statistical Model for Synthesis of Detailed Facial Geometry

Detailed surface geometry contributes greatly to the visual realism of 3D face models. However, acquiring high-resolution face geometry is often tedious and expensive. Consequently, most face models used in games, virtual reality, or computer vision look unrealistically smooth. In this paper, we introduce a new statistical technique for the analysis and synthesis of small three-dimensional facial features, such as wrinkles and pores. We acquire high-resolution face geometry for people across a wide range of ages, genders, and races. For each scan, we separate the skin surface details from a smooth base mesh using displaced subdivision surfaces. Then, we analyze the resulting displacement maps using the texture analysis/synthesis framework of Heeger and Bergen, adapted to capture statistics that vary spatially across a face. Finally, we use the extracted statistics to synthesize plausible detail on face meshes of arbitrary subjects. We demonstrate the effectiveness of this method in several applications, including analysis of facial texture in subjects with different ages and genders, interpolation between high-resolution face scans, adding detail to low-resolution face scans, and adjusting the apparent age of faces. In all cases, we are able to re-produce fine geometric details consistent with those observed in high resolution scans.Engineering and Applied Science

Beyond deep fakes: Conceptual framework, applications, and research agenda for neural rendering of realistic digital faces

Neural rendering (NR) has emerged as a novel technology for the generation and animation of realistic digital human faces. NR is based on machine learning techniques such as generative adversarial networks and is used to infer human face features and their animation from large amounts of (video) training data. NR shot to prominence with the deep fake phenomenon, the malicious and unwanted use of someone’s face for deception or satire. In this paper we demonstrate that the potential uses of NR far outstrip its use for deep fakes. We contrast NR approaches with traditional computer graphics approaches, discuss typical types of NR applications in digital face generation, and derive a conceptual framework for both guiding the design of digital characters, and for classifying existing NR use cases. We conclude with research ideas for studying the potential applications and implications of NR-based digital characters

Analysis and Construction of Engaging Facial Forms and Expressions: Interdisciplinary Approaches from Art, Anatomy, Engineering, Cultural Studies, and Psychology

The topic of this dissertation is the anatomical, psychological, and cultural examination of a human face in order to effectively construct an anatomy-driven 3D virtual face customization and action model. In order to gain a broad perspective of all aspects of a face, theories and methodology from the fields of art, engineering, anatomy, psychology, and cultural studies have been analyzed and implemented. The computer generated facial customization and action model were designed based on the collected data. Using this customization system, culturally-specific attractive face in Korean popular culture, “kot-mi-nam (flower-like beautiful guy),” was modeled and analyzed as a case study. The “kot-mi-nam” phenomenon is overviewed in textual, visual, and contextual aspects, which reveals the gender- and sexuality-fluidity of its masculinity. The analysis and the actual development of the model organically co-construct each other requiring an interwoven process. Chapter 1 introduces anatomical studies of a human face, psychological theories of face recognition and an attractive face, and state-of-the-art face construction projects in the various fields. Chapter 2 and 3 present the Bezier curve-based 3D facial customization (BCFC) and Multi-layered Facial Action Model (MFAF) based on the analysis of human anatomy, to achieve a cost-effective yet realistic quality of facial animation without using 3D scanned data. In the experiments, results for the facial customization for gender, race, fat, and age showed that BCFC achieved enhanced performance of 25.20% compared to existing program Facegen , and 44.12% compared to Facial Studio. The experimental results also proved the realistic quality and effectiveness of MFAM compared with blend shape technique by enhancing 2.87% and 0.03% of facial area for happiness and anger expressions per second, respectively. In Chapter 4, according to the analysis based on BCFC, the 3D face of an average kot-mi-nam is close to gender neutral (male: 50.38%, female: 49.62%), and Caucasian (66.42-66.40%). Culturally-specific images can be misinterpreted in different cultures, due to their different languages, histories, and contexts. This research demonstrates that facial images can be affected by the cultural tastes of the makers and can also be interpreted differently by viewers in different cultures

A head model with anatomical structure for facial modelling and animation

In this dissertation, I describe a virtual head model with anatomical structure. The model is animated in a physical-based manner by use of muscle contractions that in turn cause skin deformations; the simulation is efficient enough to achieve real-time frame rates on current PC hardware. Construction of head models is eased in my approach by deriving new models from a prototype, employing a deformation method that reshapes the complete virtual head structure. Without additional modeling tasks, this results in an immediately animatable model. The general deformation method allows for several applications such as adaptation to individual scan data for creation of animated head models of real persons. The basis for the deformation method is a set of facial feature points, which leads to other interesting uses when this set is chosen according to an anthropometric standard set of facial landmarks: I present algorithms for simulation of human head growth and reconstruction of a face from a skull.In dieser Dissertation beschreibe ich ein nach der menschlichen Anatomie strukturiertes virtuelles Kopfmodell. Dieses Modell wird physikbasiert durch Muskelkontraktionen bewegt, die wiederum Hautdeformationen hervorrufen; die Simulation ist effizient genug, um Echtzeitanimation auf aktueller PC-Hardware zu ermöglichen. Die Konstruktion eines Kopfmodells wird in meinem Ansatz durch Ableitung von einem Prototypen erleichtert, wozu eine Deformationstechnik verwendet wird, die die gesamte Struktur des virtuellen Kopfes transformiert. Ein vollständig animierbares Modell entsteht so ohne weitere Modellierungsschritte. Die allgemeine Deformationsmethode gestattet eine Vielzahl von Anwendungen, wie beispielsweise die Anpassung an individuelle Scandaten für die Erzeugung von animierten Kopfmodellen realer Personen. Die Deformationstechnik basiert auf einer Menge von Markierungspunkten im Gesicht, was zu weiteren interessanten Einsatzgebieten führt, wenn diese mit Standard- Meßpunkten aus der Anthropometrie identifiziert werden: Ich stelle Algorithmen zur Simulation des menschlichen Kopfwachstums sowie der Rekonstruktion eines Gesichtes aus Schädeldaten vor

A Survey of Computer Graphics Facial Animation Methods: Comparing Traditional Approaches to Machine Learning Methods

Human communications rely on facial expression to denote mood, sentiment, and intent. Realistic facial animation of computer graphic models of human faces can be difficult to achieve as a result of the many details that must be approximated in generating believable facial expressions. Many theoretical approaches have been researched and implemented to create more and more accurate animations that can effectively portray human emotions. Even though many of these approaches are able to generate realistic looking expressions, they typically require a lot of artistic intervention to achieve a believable result. To reduce the intervention needed to create realistic facial animation, new approaches that utilize machine learning are being researched to reduce the amount of effort needed to generate believable facial animations. This survey paper summarizes over 20 research papers related to facial animation and compares the traditional animation approaches to newer machine learning methods as well as highlights the strengths, weaknesses, and use cases of each different approach

THE REALISM OF ALGORITHMIC HUMAN FIGURES A Study of Selected Examples 1964 to 2001

It is more than forty years since the first wireframe images of the Boeing Man revealed a stylized hu-man pilot in a simulated pilot's cabin. Since then, it has almost become standard to include scenes in Hollywood movies which incorporate virtual human actors. A trait particularly recognizable in the games industry world-wide is the eagerness to render athletic muscular young men, and young women with hour-glass body-shapes, to traverse dangerous cyberworlds as invincible heroic figures. Tremendous efforts in algorithmic modeling, animation and rendering are spent to produce a realistic and believable appearance of these algorithmic humans. This thesis develops two main strands of research by the interpreting a selection of examples. Firstly, in the computer graphics context, over the forty years, it documents the development of the creation of the naturalistic appearance of images (usually called photorealism ). In particular, it de-scribes and reviews the impact of key algorithms in the course of the journey of the algorithmic human figures towards realism . Secondly, taking a historical perspective, this work provides an analysis of computer graphics in relation to the concept of realism. A comparison of realistic images of human figures throughout history with their algorithmically-generated counterparts allows us to see that computer graphics has both learned from previous and contemporary art movements such as photorealism but also taken out-of-context elements, symbols and properties from these art movements with a questionable naivety. Therefore, this work also offers a critique of the justification of the use of their typical conceptualization in computer graphics. Although the astounding technical achievements in the field of algorithmically-generated human figures are paralleled by an equally astounding disregard for the history of visual culture, from the beginning 1964 till the breakthrough 2001, in the period of the digital information processing machine, a new approach has emerged to meet the apparently incessant desire of humans to create artificial counterparts of themselves. Conversely, the theories of traditional realism have to be extended to include new problems that those active algorithmic human figures present

{3D} Morphable Face Models -- Past, Present and Future

In this paper, we provide a detailed survey of 3D Morphable Face Models over the 20 years since they were first proposed. The challenges in building and applying these models, namely capture, modeling, image formation, and image analysis, are still active research topics, and we review the state-of-the-art in each of these areas. We also look ahead, identifying unsolved challenges, proposing directions for future research and highlighting the broad range of current and future applications

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

Visual ageing of human faces in three dimensions using morphable models and projection to latent structures

We present an approach to synthesising the effects of ageing on human face images using three-dimensional modelling. We extract a set of three dimensional face models from a set of two-dimensional face images by fitting a Morphable Model. We propose a method to age these face models using Partial Least Squares to extract from the data-set those factors most related to ageing. These ageing related factors are used to train an individually weighted linear model. We show that this is an effective means of producing an aged face image and compare this method to two other linear ageing methods for ageing face models. This is demonstrated both quantitatively and with perceptual evaluation using human raters.Postprin