FacEMOTE: Qualitative Parametric Modifiers for Facial Animations

We propose a control mechanism for facial expressions by applying a few carefully chosen parametric modifications to preexisting expression data streams. This approach applies to any facial animation resource expressed in the general MPEG-4 form, whether taken from a library of preset facial expressions, captured from live performance, or entirely manually created. The MPEG-4 Facial Animation Parameters (FAPs) represent a facial expression as a set of parameterized muscle actions, given as intensity of individual muscle movements over time. Our system varies expressions by changing the intensities and scope of sets of MPEG-4 FAPs. It creates variations in “expressiveness” across the face model rather than simply scale, interpolate, or blend facial mesh node positions. The parameters are adapted from the Effort parameters of Laban Movement Analysis (LMA); we developed a mapping from their values onto sets of FAPs. The FacEMOTE parameters thus perturb a base expression to create a wide range of expressions. Such an approach could allow real-time face animations to change underlying speech or facial expression shapes dynamically according to current agent affect or user interaction needs

Final Report to NSF of the Standards for Facial Animation Workshop

The human face is an important and complex communication channel. It is a very familiar and sensitive object of human perception. The facial animation field has increased greatly in the past few years as fast computer graphics workstations have made the modeling and real-time animation of hundreds of thousands of polygons affordable and almost commonplace. Many applications have been developed such as teleconferencing, surgery, information assistance systems, games, and entertainment. To solve these different problems, different approaches for both animation control and modeling have been developed

Fully Automatic Facial Deformation Transfer

Facial Animation is a serious and ongoing challenge for the Computer Graphic industry. Because diverse and complex emotions need to be expressed by different facial deformation and animation, copying facial deformations from existing character to another is widely needed in both industry and academia, to reduce time-consuming and repetitive manual work of modeling to create the 3D shape sequences for every new character. But transfer of realistic facial animations between two 3D models is limited and inconvenient for general use. Modern deformation transfer methods require correspondences mapping, in most cases, which are tedious to get. In this paper, we present a fast and automatic approach to transfer the deformations of the facial mesh models by obtaining the 3D point-wise correspondences in the automatic manner. The key idea is that we could estimate the correspondences with different facial meshes using the robust facial landmark detection method by projecting the 3D model to the 2D image. Experiments show that without any manual labelling efforts, our method detects reliable correspondences faster and simpler compared with the state-of-the-art automatic deformation transfer method on the facial models

Measuring and simulating haemodynamics due to geometric changes in facial expression

The human brain has evolved to be very adept at recognising imperfections in human skin. In particular, observing someone’s facial skin appearance is important in recognising when someone is ill, or when finding a suitable mate. It is therefore a key goal of computer graphics research to produce highly realistic renderings of skin. However, the optical processes that give rise to skin appearance are complex and subtle. To address this, computer graphics research has incorporated more and more sophisticated models of skin reflectance. These models are generally based on static concentrations of skin chromophores; melanin and haemoglobin. However, haemoglobin concentrations are far from static, as blood flow is directly caused by both changes in facial expression and emotional state. In this thesis, we explore how blood flow changes as a consequence of changing facial expression with the aim of producing more accurate models of skin appearance. To build an accurate model of blood flow, we base it on real-world measurements of blood concentrations over time. We describe, in detail, the steps required to obtain blood concentrations from photographs of a subject. These steps are then used to measure blood concentration maps for a series of expressions that define a wide gamut of human expression. From this, we define a blending algorithm that allows us to interpolate these maps to generate concentrations for other expressions. This technique, however, requires specialist equipment to capture the maps in the first place. We try to rectify this problem by investigating a direct link between changes in facial geometry and haemoglobin concentrations. This requires building a unique capture device that captures both simultaneously. Our analysis hints a direct linear connection between the two, paving the way for further investigatio

A Data-Driven Appearance Model for Human Fatigue

Humans become visibly tired during physical activity. After a set of squats, jumping jacks or walking up a flight of stairs, individuals start to pant, sweat, loose their balance, and flush. Simulating these physiological changes due to exertion and exhaustion on an animated character greatly enhances a motion’s realism. These fatigue factors depend on the mechanical, physical, and biochemical function states of the human body. The difficulty of simulating fatigue for character animation is due in part to the complex anatomy of the human body. We present a multi-modal capturing technique for acquiring synchronized biosignal data and motion capture data to enhance character animation. The fatigue model utilizes an anatomically derived model of the human body that includes a torso, organs, face, and rigged body. This model is then driven by biosignal output. Our animations show the wide range of exhaustion behaviors synthesized from real biological data output. We demonstrate the fatigue model by augmenting standard motion capture with exhaustion effects to produce more realistic appearance changes during three exercise examples. We compare the fatigue model with both simple procedural methods and a dense marker set data capture of exercise motions

Enhanced facial expression using oxygenation absorption of facial skin

Facial skin appearance is affected by physical and physiological state of the skin. The facial expression especially the skin appearances are in constant mutability and dynamically changed as human behave, talk and stress. The color of skin is considered to be one of the key indicators for these symptoms. The skin color resolution is highly determined by the scattering and absorption of light within the skin layers. The concentration of chromophores in melanin and hemoglobin oxygenation in the blood plays a pivotal role. An improvement work on prior model to create a realistic textured three-dimensional (3D) facial model for animation is proposed. This thesis considers both surface and subsurface scattering capable of simulating the interaction of light with the human skin. Furthermore, six parameters are used in this research which are the amount of oxygenation, de-oxygenation, hemoglobin, melanin, oil and blend factor for different types of melanin in the skin to generate a perfect match to specific skin types. The proposed model is associated with Blend Shape Interpolation and Facial Action Coding System to create five basic facial emotional expressions namely anger, happy, neutral, sad and fear. Meanwhile, the correlation between blood oxygenation in changing facial skin color for basic natural emotional expressions are measured using the Pulse Oximetry and 3D skin analyzer. The data from different subjects with male and female under different number of partially extreme facial expressions are fed in the model for simulation. The multi-pole method for layered materials is used to calculate the spectral diffusion profiles of two-layered skin which are further utilized to simulate the subsurface scattering of light within the skin. While the subsurface scattering is further combined with the Torrance-Sparrow Bidirectional Reflectance Distribution Function (BRDF) model to simulate the interaction of light with an oily layer at the skin surface. The result is validated by an evaluation procedure for measuring the accountability of a facial model via expressions and skin color of proposed model to the real human. The facial expressions evaluation is verified by calculating Euclidean distance between the facial markers of the real human and the avatar. The second assessment validates the skin color of facial expressions for the proposed avatar via the extraction of Histogram Color Features and Color Coherence Vector of each image with the real human and the previous work. The experimental result shows around 5.12 percent improvement compared to previous work. In achieving the realistic facial expression for virtual human based on facial skin color, texture and oxygenation of hemoglobin, the result demonstrates that the proposed model is beneficial to the development of virtual reality and game environment of computer aided graphics animation systems

Highly automated method for facial expression synthesis

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The synthesis of realistic facial expressions has been an unexplored area for computer graphics scientists. Over the last three decades, several different construction methods have been formulated in order to obtain natural graphic results. Despite these advancements, though, current techniques still require costly resources, heavy user intervention and specific training and outcomes are still not completely realistic. This thesis, therefore, aims to achieve an automated synthesis that will produce realistic facial expressions at a low cost. This thesis, proposes a highly automated approach for achieving a realistic facial expression synthesis, which allows for enhanced performance in speed (3 minutes processing time maximum) and quality with a minimum of user intervention. It will also demonstrate a highly technical and automated method of facial feature detection, by allowing users to obtain their desired facial expression synthesis with minimal physical input. Moreover, it will describe a novel approach to the normalization of the illumination settings values between source and target images, thereby allowing the algorithm to work accurately, even in different lighting conditions. Finally, we will present the results obtained from the proposed techniques, together with our conclusions, at the end of the paper