LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning

We present a novel procedural framework to generate an arbitrary number of labeled crowd videos (LCrowdV). The resulting crowd video datasets are used to design accurate algorithms or training models for crowded scene understanding. Our overall approach is composed of two components: a procedural simulation framework for generating crowd movements and behaviors, and a procedural rendering framework to generate different videos or images. Each video or image is automatically labeled based on the environment, number of pedestrians, density, behavior, flow, lighting conditions, viewpoint, noise, etc. Furthermore, we can increase the realism by combining synthetically-generated behaviors with real-world background videos. We demonstrate the benefits of LCrowdV over prior lableled crowd datasets by improving the accuracy of pedestrian detection and crowd behavior classification algorithms. LCrowdV would be released on the WWW

Example Based Caricature Synthesis

The likeness of a caricature to the original face image is an essential and often overlooked part of caricature production. In this paper we present an example based caricature synthesis technique, consisting of shape exaggeration, relationship exaggeration, and optimization for likeness. Rather than relying on a large training set of caricature face pairs, our shape exaggeration step is based on only one or a small number of examples of facial features. The relationship exaggeration step introduces two definitions which facilitate global facial feature synthesis. The first is the T-Shape rule, which describes the relative relationship between the facial elements in an intuitive manner. The second is the so called proportions, which characterizes the facial features in a proportion form. Finally we introduce a similarity metric as the likeness metric based on the Modified Hausdorff Distance (MHD) which allows us to optimize the configuration of facial elements, maximizing likeness while satisfying a number of constraints. The effectiveness of our algorithm is demonstrated with experimental results

3D (embodied) projection mapping and sensing bodies : a study in interactive dance performance

This dissertation identifies the synergies between physical and virtual environments when designing for immersive experiences in interactive dance performances. The integration of virtual information in physical space is transforming our interactions and experiences with the world. By using the body and creative expression as the interface between real and virtual worlds, dance performance creates a privileged framework to research and design interactive mixed reality environments and immersive augmented architectures. The research is primarily situated in the fields of visual art and interaction design. It combines performance with transdisciplinary fields and intertwines practice with theory. The theoretical and conceptual implications involved in designing and experiencing immersive hybrid environments are analyzed using the reality–virtuality continuum. These theories helped frame the ways augmented reality architectures are achieved through the integration of dance performance with digital software and reception displays. They also helped identify the main artistic affordances and restrictions in the design of augmented reality and augmented virtuality environments for live performance. These pervasive media architectures were materialized in three field experiments, the live dance performances. Each performance was created in three different stages of conception, design and production. The first stage was to “digitize” the performer’s movement and brain activity to the virtual environment and our system. This was accomplished through the use of depth sensor cameras, 3D motion capture, and brain computer interfaces. The second stage was the creation of the computational architecture and software that aggregates the connections and mapping between the physical body and the spatial dynamics of the virtual environment. This process created real-time interactions between the performer’s behavior and motion and the real-time generative computer 3D graphics. Finally, the third stage consisted of the output modality: 3D projector based augmentation techniques were adopted in order to overlay the virtual environment onto physical space. This thesis proposes and lays out theoretical, technical, and artistic frameworks between 3D digital environments and moving bodies in dance performance. By sensing the body and the brain with the 3D virtual environments, new layers of augmentation and interactions are established, and ultimately this generates mixed reality environments for embodied improvisational self-expression.Radio-Television-Fil

06241 Abstracts Collection -- Human Motion - Understanding, Modeling, Capture and Animation. 13th Workshop

From 11.06.06 to 16.06.06, the Dagstuhl Seminar 06241 ``Human Motion - Understanding, Modeling, Capture and Animation. 13th Workshop "Theoretical Foundations of Computer Vision"\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general

AFFECT-PRESERVING VISUAL PRIVACY PROTECTION

The prevalence of wireless networks and the convenience of mobile cameras enable many new video applications other than security and entertainment. From behavioral diagnosis to wellness monitoring, cameras are increasing used for observations in various educational and medical settings. Videos collected for such applications are considered protected health information under privacy laws in many countries. Visual privacy protection techniques, such as blurring or object removal, can be used to mitigate privacy concern, but they also obliterate important visual cues of affect and social behaviors that are crucial for the target applications. In this dissertation, we propose to balance the privacy protection and the utility of the data by preserving the privacy-insensitive information, such as pose and expression, which is useful in many applications involving visual understanding. The Intellectual Merits of the dissertation include a novel framework for visual privacy protection by manipulating facial image and body shape of individuals, which: (1) is able to conceal the identity of individuals; (2) provide a way to preserve the utility of the data, such as expression and pose information; (3) balance the utility of the data and capacity of the privacy protection. The Broader Impacts of the dissertation focus on the significance of privacy protection on visual data, and the inadequacy of current privacy enhancing technologies in preserving affect and behavioral attributes of the visual content, which are highly useful for behavior observation in educational and medical settings. This work in this dissertation represents one of the first attempts in achieving both goals simultaneously

Stable Real-Time Interaction Between Virtual Humans and Real Scenes

We present an augmented reality system that relies on purely passive techniques to solve the real-time registration problem. It can run on a portable PC and does not require engineering of the environment, for example by adding markers. To achieve this result, we have integrated robust computer vision techniques into a powerful VR framework. The resulting AR system allows us to produce complex rendering and animation of virtual human characters, and to blend them into the real world. The system tracks the 3D camera position by means of a natural features tracker, which, given a rough CAD model, can deal with complex 3D objects. The tracking method can handle both large camera displacements and aspect changes. We will show that our system works in the cluttered environment of a real industrial facility and can, therefore, be used to enhance manufacturing and industrial processe

{3D} Morphable Face Models -- Past, Present and Future

In this paper, we provide a detailed survey of 3D Morphable Face Models over the 20 years since they were first proposed. The challenges in building and applying these models, namely capture, modeling, image formation, and image analysis, are still active research topics, and we review the state-of-the-art in each of these areas. We also look ahead, identifying unsolved challenges, proposing directions for future research and highlighting the broad range of current and future applications