Synthesis and Control of High Resolution Facial Expressions for Visual Interactions

The synthesis of facial expression with control of intensity and personal styles is important in intelligent and affective human-computer interaction, especially in face-to-face inter-action between human and intelligent agent. We present a facial expression animation system that facilitates control of expressiveness and style. We learn a decomposable genera-tive model for the nonlinear deformation of facial expressions by analyzing the mapping space between low dimensional embedded representation and high resolution tracking data. Bilinear analysis of the mapping space provides a compact representation of the nonlinear generative model for facial expressions. The decomposition allows synthesis of new fa-cial expressions by control of geometry and expression style. The generative model provides control of expressiveness pre-serving nonlinear deformation in the expressions with simple parameters and allows synthesis of stylized facial geometry. In addition, we can directly extract the MPEG-4 Facial Ani-mation Parameters (FAPs) from the synthesized data, which allows using any animation engine that supports FAPs to ani-mate new synthesized expressions. 1

SYNTHESIS OF SPECIFIC UTTERANCES AND EMOTIONAL EXPRESSIONS USING 3D FACE IMAGES

In previous research, there was a study that applied principal component analysis to face data expressed by high-dimensional vectors, expressed them with low-dimensional parameters, and generated faces with various impressions by changing the parameters. It began with the change of impressions such as gender, age, and race as the impression transformation vector method, and extended to the generation of utterance expressions during conversation. However, the utterance expression was based on a true face. Therefore, in this study, we performed a study to generate facial expressions with two impressions by adjusting the ratio of parameter change when applying the impression transformation vector method for each region

Automatic 3D Facial Expression Analysis in Videos

We introduce a novel framework for automatic 3D facial expression analysis in videos. Preliminary results demonstrate editing facial expression with facial expression recognition. We first build a 3D expression database to learn the expression space of a human face. The real-time 3D video data were captured by a camera/projector scanning system. From this database, we extract the geometry deformation independent of pose and illumination changes. All possible facial deformations of an individual make a nonlinear manifold embedded in a high dimensional space. To combine the manifolds of different subjects that vary significantly and are usually hard to align, we transfer the facial deformations in all training videos to one standard model. Lipschitz embedding embeds the normalized deformation of the standard model in a low dimensional generalized manifold. We learn a probabilistic expression model on the generalized manifold. To edit a facial expression of a new subject in 3D videos, the system searches over this generalized manifold for optimal replacement with the 'target' expression, which will be blended with the deformation in the previous frames to synthesize images of the new expression with the current head pose. Experimental results show that our method works effectively

THREE DIMENSIONAL MODELING AND ANIMATION OF FACIAL EXPRESSIONS

Facial expression and animation are important aspects of the 3D environment featuring human characters. These animations are frequently used in many kinds of applications and there have been many efforts to increase the realism. Three aspects are still stimulating active research: the detailed subtle facial expressions, the process of rigging a face, and the transfer of an expression from one person to another. This dissertation focuses on the above three aspects. A system for freely designing and creating detailed, dynamic, and animated facial expressions is developed. The presented pattern functions produce detailed and animated facial expressions. The system produces realistic results with fast performance, and allows users to directly manipulate it and see immediate results. Two unique methods for generating real-time, vivid, and animated tears have been developed and implemented. One method is for generating a teardrop that continually changes its shape as the tear drips down the face. The other is for generating a shedding tear, which is a kind of tear that seamlessly connects with the skin as it flows along the surface of the face, but remains an individual object. The methods both broaden CG and increase the realism of facial expressions. A new method to automatically set the bones on facial/head models to speed up the rigging process of a human face is also developed. To accomplish this, vertices that describe the face/head as well as relationships between each part of the face/head are grouped. The average distance between pairs of vertices is used to place the head bones. To set the bones in the face with multi-density, the mean value of the vertices in a group is measured. The time saved with this method is significant. A novel method to produce realistic expressions and animations by transferring an existing expression to a new facial model is developed. The approach is to transform the source model into the target model, which then has the same topology as the source model. The displacement vectors are calculated. Each vertex in the source model is mapped to the target model. The spatial relationships of each mapped vertex are constrained

Face and facial expression analysis based on an active appearance model

In this paper, methods are proposed for facial feature detection (eyes, brows, nose, mouth, chin) and for facial expression recognition. The methods are based on modified versions of the standard Active Appearance Model proposed by Cootes et al. [11] to control both the shape and the texture of a given face. The detection algorithm makes use of an active appearance model computed on hierarchical Gabor descriptions a set of training faces. In a second part, two expression models are proposed, based on the standard AAM, and used to recognize and then to cancel or modify the facial expression of a given unknown face.Dans cet article, nous nous intéressons à l'extraction automatique des traits de visages (yeux, sourcils, nez, bouche, menton) ainsi qu'à la reconnaissance des six expressions faciales définies par Ekman [19]. Nous exploitons pour cela des versions modifiées du modèle actif d'apparence initialement proposé par Cootes et al. [11] qui permet de représenter à la fois la forme et la texture d'un visage. L'extraction des traits faciaux est faite à l'aide d'un modèle actif d'apparence hiérarchique, calculé à partir des réponses de visages à des bancs de filtres de Gabor. Deux modèles d'expressions faciales sont ensuite proposés, calculés à pâtir du modèle d'apparence standard (non hiérarchique), pour reconnaître puis supprimer ou modifier l'expression d'un visage inconnu

Statistical modelling for facial expression dynamics

PhDOne of the most powerful and fastest means of relaying emotions between humans are facial expressions. The ability to capture, understand and mimic those emotions and their underlying dynamics in the synthetic counterpart is a challenging task because of the complexity of human emotions, different ways of conveying them, non-linearities caused by facial feature and head motion, and the ever critical eye of the viewer. This thesis sets out to address some of the limitations of existing techniques by investigating three components of expression modelling and parameterisation framework: (1) Feature and expression manifold representation, (2) Pose estimation, and (3) Expression dynamics modelling and their parameterisation for the purpose of driving a synthetic head avatar. First, we introduce a hierarchical representation based on the Point Distribution Model (PDM). Holistic representations imply that non-linearities caused by the motion of facial features, and intrafeature correlations are implicitly embedded and hence have to be accounted for in the resulting expression space. Also such representations require large training datasets to account for all possible variations. To address those shortcomings, and to provide a basis for learning more subtle, localised variations, our representation consists of tree-like structure where a holistic root component is decomposed into leaves containing the jaw outline, each of the eye and eyebrows and the mouth. Each of the hierarchical components is modelled according to its intrinsic functionality, rather than the final, holistic expression label. Secondly, we introduce a statistical approach for capturing an underlying low-dimension expression manifold by utilising components of the previously defined hierarchical representation. As Principal Component Analysis (PCA) based approaches cannot reliably capture variations caused by large facial feature changes because of its linear nature, the underlying dynamics manifold for each of the hierarchical components is modelled using a Hierarchical Latent Variable Model (HLVM) approach. Whilst retaining PCA properties, such a model introduces a probability density model which can deal with missing or incomplete data and allows discovery of internal within cluster structures. All of the model parameters and underlying density model are automatically estimated during the training stage. We investigate the usefulness of such a model to larger and unseen datasets. Thirdly, we extend the concept of HLVM model to pose estimation to address the non-linear shape deformations and definition of the plausible pose space caused by large head motion. Since our head rarely stays still, and its movements are intrinsically connected with the way we perceive and understand the expressions, pose information is an integral part of their dynamics. The proposed 3 approach integrates into our existing hierarchical representation model. It is learned using sparse and discreetly sampled training dataset, and generalises to a larger and continuous view-sphere. Finally, we introduce a framework that models and extracts expression dynamics. In existing frameworks, explicit definition of expression intensity and pose information, is often overlooked, although usually implicitly embedded in the underlying representation. We investigate modelling of the expression dynamics based on use of static information only, and focus on its sufficiency for the task at hand. We compare a rule-based method that utilises the existing latent structure and provides a fusion of different components with holistic and Bayesian Network (BN) approaches. An Active Appearance Model (AAM) based tracker is used to extract relevant information from input sequences. Such information is subsequently used to define the parametric structure of the underlying expression dynamics. We demonstrate that such information can be utilised to animate a synthetic head avatar. Submitte