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

Reconstruction and analysis of dynamic shapes

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 122-141).Motion capture has revolutionized entertainment and influenced fields as diverse as the arts, sports, and medicine. This is despite the limitation that it tracks only a small set of surface points. On the other hand, 3D scanning techniques digitize complete surfaces of static objects, but are not applicable to moving shapes. I present methods that overcome both limitations, and can obtain the moving geometry of dynamic shapes (such as people and clothes in motion) and analyze it in order to advance computer animation. Further understanding of dynamic shapes will enable various industries to enhance virtual characters, advance robot locomotion, improve sports performance, and aid in medical rehabilitation, thus directly affecting our daily lives. My methods efficiently recover much of the expressiveness of dynamic shapes from the silhouettes alone. Furthermore, the reconstruction quality is greatly improved by including surface orientations (normals). In order to make reconstruction more practical, I strive to capture dynamic shapes in their natural environment, which I do by using hybrid inertial and acoustic sensors. After capture, the reconstructed dynamic shapes are analyzed in order to enhance their utility. My algorithms then allow animators to generate novel motions, such as transferring facial performances from one actor onto another using multi-linear models. The presented research provides some of the first and most accurate reconstructions of complex moving surfaces, and is among the few approaches that establish a relationship between different dynamic shapes.by Daniel Vlasic.Ph.D

Realtime Face Tracking and Animation

Capturing and processing human geometry, appearance, and motion is at the core of computer graphics, computer vision, and human-computer interaction. The high complexity of human geometry and motion dynamics, and the high sensitivity of the human visual system to variations and subtleties in faces and bodies make the 3D acquisition and reconstruction of humans in motion a challenging task. Digital humans are often created through a combination of 3D scanning, appearance acquisition, and motion capture, leading to stunning results in recent feature films. However, these methods typically require complex acquisition systems and substantial manual post-processing. As a result, creating and animating high-quality digital avatars entails long turn-around times and substantial production costs. Recent technological advances in RGB-D devices, such as Microsoft Kinect, brought new hopes for realtime, portable, and affordable systems allowing to capture facial expressions as well as hand and body motions. RGB-D devices typically capture an image and a depth map. This permits to formulate the motion tracking problem as a 2D/3D non-rigid registration of a deformable model to the input data. We introduce a novel face tracking algorithm that combines geometry and texture registration with pre-recorded animation priors in a single optimization. This led to unprecedented face tracking quality on a low cost consumer level device. The main drawback of this approach in the context of consumer applications is the need for an offline user-specific training. Robust and efficient tracking is achieved by building an accurate 3D expression model of the user's face who is scanned in a predefined set of facial expressions. We extended this approach removing the need of a user-specific training or calibration, or any other form of manual assistance, by modeling online a 3D user-specific dynamic face model. In complement of a realtime face tracking and modeling algorithm, we developed a novel system for animation retargeting that allows learning a high-quality mapping between motion capture data and arbitrary target characters. We addressed one of the main challenges of existing example-based retargeting methods, the need for a large number of accurate training examples to define the correspondence between source and target expression spaces. We showed that this number can be significantly reduced by leveraging the information contained in unlabeled data, i.e. facial expressions in the source or target space without corresponding poses. Finally, we present a novel realtime physics-based animation technique allowing to simulate a large range of deformable materials such as fat, flesh, hair, or muscles. This approach could be used to produce more lifelike animations by enhancing the animated avatars with secondary effects. We believe that the realtime face tracking and animation pipeline presented in this thesis has the potential to inspire numerous future research in the area of computer-generated animation. Already, several ideas presented in thesis have been successfully used in industry and this work gave birth to the startup company faceshift AG

Inverse rendering for scene reconstruction in general environments

Demand for high-quality 3D content has been exploding recently, owing to the advances in 3D displays and 3D printing. However, due to insufficient 3D content, the potential of 3D display and printing technology has not been realized to its full extent. Techniques for capturing the real world, which are able to generate 3D models from captured images or videos, are a hot research topic in computer graphics and computer vision. Despite significant progress, many methods are still highly constrained and require lots of prerequisites to succeed. Marker-less performance capture is one such dynamic scene reconstruction technique that is still confined to studio environments. The requirements involved, such as the need for a multi-view camera setup, specially engineered lighting or green-screen backgrounds, prevent these methods from being widely used by the film industry or even by ordinary consumers. In the area of scene reconstruction from images or videos, this thesis proposes new techniques that succeed in general environments, even using as few as two cameras. Contributions are made in terms of reducing the constraints of marker-less performance capture on lighting, background and the required number of cameras. The primary theoretical contribution lies in the investigation of light transport mechanisms for high-quality 3D reconstruction in general environments. Several steps are taken to approach the goal of scene reconstruction in general environments. At first, the concept of employing inverse rendering for scene reconstruction is demonstrated on static scenes, where a high-quality multi-view 3D reconstruction method under general unknown illumination is developed. Then, this concept is extended to dynamic scene reconstruction from multi-view video, where detailed 3D models of dynamic scenes can be captured under general and even varying lighting, and in front of a general scene background without a green screen. Finally, efforts are made to reduce the number of cameras employed. New performance capture methods using as few as two cameras are proposed to capture high-quality 3D geometry in general environments, even outdoors.Die Nachfrage nach qualitativ hochwertigen 3D Modellen ist in letzter Zeit, bedingt durch den technologischen Fortschritt bei 3D-Wieder-gabegeräten und -Druckern, stark angestiegen. Allerdings konnten diese Technologien wegen mangelnder Inhalte nicht ihr volles Potential entwickeln. Methoden zur Erfassung der realen Welt, welche 3D-Modelle aus Bildern oder Videos generieren, sind daher ein brandaktuelles Forschungsthema im Bereich Computergrafik und Bildverstehen. Trotz erheblichen Fortschritts in dieser Richtung sind viele Methoden noch stark eingeschränkt und benötigen viele Voraussetzungen um erfolgreich zu sein. Markerloses Performance Capturing ist ein solches Verfahren, das dynamische Szenen rekonstruiert, aber noch auf Studio-Umgebungen beschränkt ist. Die spezifischen Anforderung solcher Verfahren, wie zum Beispiel einen Mehrkameraaufbau, maßgeschneiderte, kontrollierte Beleuchtung oder Greenscreen-Hintergründe verhindern die Verbreitung dieser Verfahren in der Filmindustrie und besonders bei Endbenutzern. Im Bereich der Szenenrekonstruktion aus Bildern oder Videos schlägt diese Dissertation neue Methoden vor, welche in beliebigen Umgebungen und auch mit nur wenigen (zwei) Kameras funktionieren. Dazu werden Schritte unternommen, um die Einschränkungen bisheriger Verfahren des markerlosen Performance Capturings im Hinblick auf Beleuchtung, Hintergründe und die erforderliche Anzahl von Kameras zu verringern. Der wichtigste theoretische Beitrag liegt in der Untersuchung von Licht-Transportmechanismen für hochwertige 3D-Rekonstruktionen in beliebigen Umgebungen. Dabei werden mehrere Schritte unternommen, um das Ziel der Szenenrekonstruktion in beliebigen Umgebungen anzugehen. Zunächst wird die Anwendung von inversem Rendering auf die Rekonstruktion von statischen Szenen dargelegt, indem ein hochwertiges 3D-Rekonstruktionsverfahren aus Mehransichtsaufnahmen unter beliebiger, unbekannter Beleuchtung entwickelt wird. Dann wird dieses Konzept auf die dynamische Szenenrekonstruktion basierend auf Mehransichtsvideos erweitert, wobei detaillierte 3D-Modelle von dynamischen Szenen unter beliebiger und auch veränderlicher Beleuchtung vor einem allgemeinen Hintergrund ohne Greenscreen erfasst werden. Schließlich werden Anstrengungen unternommen die Anzahl der eingesetzten Kameras zu reduzieren. Dazu werden neue Verfahren des Performance Capturings, unter Verwendung von lediglich zwei Kameras vorgeschlagen, um hochwertige 3D-Geometrie im beliebigen Umgebungen, sowie im Freien, zu erfassen