On combining the facial movements of a talking head

We present work on Obie, an embodied conversational agent framework. An embodied conversational agent, or talking head, consists of three main components. The graphical part consists of a face model and a facial muscle model. Besides the graphical part, we have implemented an emotion model and a mapping from emotions to facial expressions. The animation part of the framework focuses on the combination of different facial movements temporally. In this paper we propose a scheme of combining facial movements on a 3D talking head

Improvements on a simple muscle-based 3D face for realistic facial expressions

Facial expressions play an important role in face-to-face communication. With the development of personal computers capable of rendering high quality graphics, computer facial animation has produced more and more realistic facial expressions to enrich human-computer communication. In this paper, we present a simple muscle-based 3D face model that can produce realistic facial expressions in real time. We extend Waters' (1987) muscle model to generate bulges and wrinkles and to improve the combination of multiple muscle actions. In addition, we present techniques to reduce the computation burden on the muscle mode

Capture, Learning, and Synthesis of 3D Speaking Styles

Audio-driven 3D facial animation has been widely explored, but achieving realistic, human-like performance is still unsolved. This is due to the lack of available 3D datasets, models, and standard evaluation metrics. To address this, we introduce a unique 4D face dataset with about 29 minutes of 4D scans captured at 60 fps and synchronized audio from 12 speakers. We then train a neural network on our dataset that factors identity from facial motion. The learned model, VOCA (Voice Operated Character Animation) takes any speech signal as input - even speech in languages other than English - and realistically animates a wide range of adult faces. Conditioning on subject labels during training allows the model to learn a variety of realistic speaking styles. VOCA also provides animator controls to alter speaking style, identity-dependent facial shape, and pose (i.e. head, jaw, and eyeball rotations) during animation. To our knowledge, VOCA is the only realistic 3D facial animation model that is readily applicable to unseen subjects without retargeting. This makes VOCA suitable for tasks like in-game video, virtual reality avatars, or any scenario in which the speaker, speech, or language is not known in advance. We make the dataset and model available for research purposes at http://voca.is.tue.mpg.de.Comment: To appear in CVPR 201

Issues in Facial Animation

Our goal is to build a system of 3-D animation of facial expressions of emotion correlated with the intonation of the voice. Up till now, the existing systems did not take into account the link between these two features. Many linguists and psychologists have noted the importance of spoken intonation for conveying different emotions associated with speakers\u27 messages. Moreover, some psychologists have found some universal facial expressions linked to emotions and attitudes. We will look at the rules that control these relations (intonation/emotions and facial expressions/emotions) as well as the coordination of these various modes of expressions. Given an utterance, we consider how the message (what is new/old information in the given context) transmitted through the choice of accents and their placement, are conveyed through the face. The facial model integrates the action of each muscle or group of muscles as well as the propagation of the muscles\u27 movement. It is also adapted to the FACS notation (Facial Action Coding System) created by P. Ekman and W. Friesen to describe facial expressions. Our first step will be to enumerate and to differentiate facial movements linked to emotions from the ones linked to conversation. Then, we will examine what the rules are that drive them and how their different actions interact

Creative tools for producing realistic 3D facial expressions and animation

Creative exploration of realistic 3D facial animation is a popular but very challenging task due to the high level knowledge and skills required. This forms a barrier for creative individuals who have limited technical skills but wish to explore their creativity in this area. This paper proposes a new technique that facilitates users’ creative exploration by hiding the technical complexities of producing facial expressions and animation. The proposed technique draws on research from psychology, anatomy and employs Autodesk Maya as a use case by developing a creative tool, which extends Maya’s Blend Shape Editor. User testing revealed that novice users in the creative media, employing the proposed tool can produce rich and realistic facial expressions that portray new interesting emotions. It reduced production time by 25% when compared to Maya and by 40% when compared to 3DS Max equivalent tools

Animating Through Warping: an Efficient Method for High-Quality Facial Expression Animation

Advances in deep neural networks have considerably improved the art of animating a still image without operating in 3D domain. Whereas, prior arts can only animate small images (typically no larger than 512x512) due to memory limitations, difficulty of training and lack of high-resolution (HD) training datasets, which significantly reduce their potential for applications in movie production and interactive systems. Motivated by the idea that HD images can be generated by adding high-frequency residuals to low-resolution results produced by a neural network, we propose a novel framework known as Animating Through Warping (ATW) to enable efficient animation of HD images. Specifically, the proposed framework consists of two modules, a novel two-stage neural-network generator and a novel post-processing module known as Animating Through Warping (ATW). It only requires the generator to be trained on small images and can do inference on an image of any size. During inference, an HD input image is decomposed into a low-resolution component(128x128) and its corresponding high-frequency residuals. The generator predicts the low-resolution result as well as the motion field that warps the input face to the desired status (e.g., expressions categories or action units). Finally, the ResWarp module warps the residuals based on the motion field and adding the warped residuals to generates the final HD results from the naively up-sampled low-resolution results. Experiments show the effectiveness and efficiency of our method in generating high-resolution animations. Our proposed framework successfully animates a 4K facial image, which has never been achieved by prior neural models. In addition, our method generally guarantee the temporal coherency of the generated animations. Source codes will be made publicly available.Comment: 18 pages, 13 figures, Accepted to ACM Multimedia 202

A multimedia testbed for facial animation control

This paper presents an open testbed for controlling facial animation. The adopted controlling means can act at different levels of abstraction (specification). These means of control can be associated with different interactive devices and media thereby allowing a greater flexibility and freedom to the animator. Possibility of integration and mixing of control means provides a general platform where a user can experiment with his choice of control method. Experiments with input accessories like the keyboard of a music sinthesizer and gestures from the DataGlove are illustrated.59-7