Prominence Driven Character Animation

This paper details the development of a fully automated system for character animation implemented in Autodesk Maya. The system uses prioritised speech events to algorithmically generate head, body, arms and leg movements alongside eyeblinks, eyebrow movements and lip-synching. In addition, gaze tracking is also generated automatically relative to the definition of focus objects- contextually important objects in the character\u27s worldview. The plugin uses an animation profile to store the relevant controllers and movements for a specific character, allowing any character to run with the system. Once a profile has been created, an audio file can be loaded and animated with a single button click. The average time to animate is between 2-3 minutes for 1 minute of speech, and the plugin can be used either as a first pass system for high quality work or as part of a batch animation workflow for larger amounts of content as exemplified in television and online dissemination channels

The Relationship Between Nonverbal Immediacy and Therapeutic Alliance in Higher Education Client-Counselor Interactions

Communication is essential to a successful patient-provider interaction. Within health communication literature, a substantial body of research has focused on verbal communication; however, few studies have fully dedicated to nonverbal communication research. The study examined the relationship between perceptions of client nonverbal immediacy and ratings of the therapeutic alliance. Additionally, the study provided an analysis comparing counselor and client ratings of both client nonverbal immediacy and the therapeutic alliance. Results indicated a significant relationship between counselor ratings of client nonverbal immediacy and counselor ratings of the therapeutic alliance. Counselors and clients also rated client nonverbal immediacy similarly, indicating that the counselors are aware of their client’s behavior. Counselors and clients also rated the therapeutic alliance similarly

Personagens virtuais em ambientes virtuais (Plataforma IViHumans)

Tese de mestrado em Engenharia Informática, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2010Este projecto enquadra-se no desenvolvimento da plataforma IViHumans, que suporta a representação de Humanos Virtuais Inteligentes inseridos em ambientes virtuais. Neste contexto é fundamental conceber modelos de humanos virtuais credíveis e animados para povoar esses mundos sintéticos, uma tarefa que exige um esforço de modelação considerável e um consumo de tempo significativo. Adicionalmente, é importante dominar a ferramenta de modelação 3D com que se trabalha, e ter um bom desempenho artístico na sua utilização. Com o intuito de tornar esta tarefa menos trabalhosa para o animador, foi concebida uma abordagem semiautomática para a aplicação de animações idênticas a diferentes modelos de humanos virtuais. A solução desenvolvida facilita a tarefa do animador, mas não dispensa a sua intervenção para proceder a pequenos ajustes, sendo por isso “semiautomática”. Foi implementada no Autodesk 3D Studio Max 2010, recorrendo às suas capacidades de modelação e à linguagem interna de scripting desta ferramenta.This project is part of the IViHumans platform, which supports the representation of Intelligent Virtual Humans inserted in virtual environments. One of our main concerns is to obtain credible and animated virtual human models to populate these synthetic worlds, a task that requires considerable modeling effort and significant time consumption. Furthermore, it is important to master the use of a 3D modeling tool and to possess good modeling skills. Aiming to ease the animator’s task, we implemented a semi-automatic approach that supports the aplication of identical animations to different virtual humans models. The developed solution eases the animator’s task, but his intervention is still needed to perform some minor adjustments, it is therefore ”semi-automatic”. It was developed in Autodesk 3D Studio Max 2010, using its modeling capabilities and its scripting language

Sintesis Ekspresi Wajah Realistik Berbasis Feature-Point Cluster Menggunakan Radial Basis Function

Meningkatnya permintaan produk animasi oleh rumah produksi dan stasiun televisi menuntut adanya perubahan yang signifikan di dalam proses produksi animasi. Penelitian animasi ekspresi pada wajah khususnya mengenai proses rigging dan pemindahan ekspresi semakin banyak. Pendekatan tradisional animasi ekspresi wajah sangat tergantung pada animator dalam pembuatan gerakan kunci dan rangkaian gerakan ekspresi wajah. Hal ini menyebabkan produksi animasi wajah untuk satu wajah tidak dapat digunakan ulang secara langsung untuk wajah lainnya karena kekhususannya tersebut. Oleh karena itu proses otomatisasi pembentukan area pembobotan pada model wajah 3D dengan pendekatan cluster berikut proses duplikasi gerak yang adaptif terhadap bentuk wajah untuk mempersingkat proses produksi animasi sangat penting. Prinsip animasi dipandang sebagai salah satu solusi dan panduan untuk pembuatan animasi gerak wajah yang ekspresif dan hidup. Sintesis ekspresi wajah realistik dapat dibuat dengan basis feature-point cluster menggunakan radial basis function. Otomatisasi pembentukan area gerak di wajah hasil proses clustering berdasarkan letak fitur titik dan proses retargeting menggunakan radial basis function untuk melakukan sintesis ekspresi wajah realistik merupakan kebaruan yang diangkat pada penelitian ini. Berdasarkan semua tahapan eksperimentasi yang dilakukan dapat disimpulkan bahwa sintesis ekspresi wajah realistik dengan basis feature-point cluster menggunakan radial basis function dapat diterapkan pada beragam model wajah 3D dan dapat secara adaptif peka terhadap bentuk wajah dari masing-masing model 3D yang memiliki jumlah fitur penanda yang sama. Hasil persepsi visual evaluasi penerapan sintesis ekspresi wajah realistik menunjukkan hasil ekspresi terkejut memiliki persentasi paling tinggi mudah dikenali, yaitu: 89,32%. Ekspresi senang: 84,63 %, ekspresi sedih: 77,32%, ekspresi marah: 76,64%, ekspresi jijik: 76,45%, serta ekspresi takut: 76,44%. Rerata persentase wajah mudah dikenali sebesar 80,13%. ================================================================================================================== The increasing demand of animated movies by production houses and television stations needs a significant change in the animation production process. Computer facial animation research on the process of rigging and expression transfer is growing. The traditional approach of facial animation is highly dependent on the animator in making the key and the sequence of facial expression movements. This causes the production of facial animation for one face can not be reused directly for the other face because of its uniqueness. Therefore, the process of automating the formation of weighted areas on 3D face model with cluster approach and adaptive motion transfer process to face shape is very important to shorten the production process of animation. The principle of animation is seen as one of the solutions and guidelines for the creation of animated facial expression expressively. The synthesis of realistic facial expression can be made on the basis of a feature-point cluster using a radial basis function. Automation process for formatting the motion area in the face by clustering process based on the location of the feature-point and retargeting process using radial basis function to perform synthesis of realistic facial expression is the novelty of this research. Based on all experimentation stages, it can be concluded that the synthesis of realistic facial expression based on a feature-point cluster using radial basis function can be applied to various 3D face models and can be adaptively sensitive to the facial shape of each 3D model which has the same number of marker features. The results of visual perception evaluation from the synthesis of realistic facial expression show that surprise expression has the highest percentage and easily recognizable, 89,32%. Happy expression: 84,63%, sad expression: 77,32%, angry expression: 76,64%, disgust expression: 76,45%, and a fear expression: 76,44%. The average percentage of faces is easily recognizable at 80,13%

Expressive facial gestures from motion capture data

Human facial gestures often exhibit such natural stochastic variations as how often the eyes blink, how often the eyebrows and the nose twitch, and how the head moves while speaking. The stochastic movements of facial features are key ingredients for generating convincing facial expressions. Although such small variations have been simulated using noise functions in many graphics applications, modulating noise functions to match natural variations induced from the affective states and the personality of characters is difficult and not intuitive. We present a technique for generating subtle expressive facial gestures (facial expressions and head motion) semiautomatically from motion capture data. Our approach is based on Markov random fields that are simulated in two levels. In the lower level, the coordinated movements of facial features are captured, parameterized, and transferred to synthetic faces using basis shapes. The upper level represents independent stochastic behavior of facial features. The experimental results show that our system generates expressive facial gestures synchronized with input speech

Robust visual speech recognition using optical flow analysis and rotation invariant features

The focus of this thesis is to develop computer vision algorithms for visual speech recognition system to identify the visemes. The majority of existing speech recognition systems is based on audio-visual signals and has been developed for speech enhancement and is prone to acoustic noise. Considering this problem, aim of this research is to investigate and develop a visual only speech recognition system which should be suitable for noisy environments. Potential applications of such a system include the lip-reading mobile phones, human computer interface (HCI) for mobility-impaired users, robotics, surveillance, improvement of speech based computer control in a noisy environment and for the rehabilitation of the persons who have undergone a laryngectomy surgery. In the literature, there are several models and algorithms available for visual feature extraction. These features are extracted from static mouth images and characterized as appearance and shape based features. However, these methods rarely incorporate the time dependent information of mouth dynamics. This dissertation presents two optical flow based approaches of visual feature extraction, which capture the mouth motions in an image sequence. The motivation for using motion features is, because the human perception of lip-reading is concerned with the temporal dynamics of mouth motion. The first approach is based on extraction of features from the optical flow vertical component. The optical flow vertical component is decomposed into multiple non-overlapping fixed scale blocks and statistical features of each block are computed for successive video frames of an utterance. To overcome the issue of large variation in speed of speech, each utterance is normalized using simple linear interpolation method. In the second approach, four directional motion templates based on optical flow are developed, each representing the consolidated motion information in an utterance in four directions (i.e.,up, down, left and right). This approach is an evolution of a view based approach known as motion history image (MHI). One of the main issues with the MHI method is its motion overwriting problem because of self-occlusion. DMHIs seem to solve this issue of overwriting. Two types of image descriptors, Zernike moments and Hu moments are used to represent each image of DMHIs. A support vector machine (SVM) classifier was used to classify the features obtained from the optical flow vertical component, Zernike and Hu moments separately. For identification of visemes, a multiclass SVM approach was employed. A video speech corpus of seven subjects was used for evaluating the efficiency of the proposed methods for lip-reading. The experimental results demonstrate the promising performance of the optical flow based mouth movement representations. Performance comparison between DMHI and MHI based on Zernike moments, shows that the DMHI technique outperforms the MHI technique. A video based adhoc temporal segmentation method is proposed in the thesis for isolated utterances. It has been used to detect the start and the end frame of an utterance from an image sequence. The technique is based on a pair-wise pixel comparison method. The efficiency of the proposed technique was tested on the available data set with short pauses between each utterance