Automatic 3D human modeling: an initial stage towards 2-way inside interaction in mixed reality

3D human models play an important role in computer graphics applications from a wide range of domains, including education, entertainment, medical care simulation and military training. In many situations, we want the 3D model to have a visual appearance that matches that of a specific living person and to be able to be controlled by that person in a natural manner. Among other uses, this approach supports the notion of human surrogacy, where the virtual counterpart provides a remote presence for the human who controls the virtual character\u27s behavior. In this dissertation, a human modeling pipeline is proposed for the problem of creating a 3D digital model of a real person. Our solution involves reshaping a 3D human template with a 2D contour of the participant and then mapping the captured texture of that person to the generated mesh. Our method produces an initial contour of a participant by extracting the user image from a natural background. One particularly novel contribution in our approach is the manner in which we improve the initial vertex estimate. We do so through a variant of the ShortStraw corner-finding algorithm commonly used in sketch-based systems. Here, we develop improvements to ShortStraw, presenting an algorithm called IStraw, and then introduce adaptations of this improved version to create a corner-based contour segmentatiuon algorithm. This algorithm provides significant improvements on contour matching over previously developed systems, and does so with low computational complexity. The system presented here advances the state of the art in the following aspects. First, the human modeling process is triggered automatically by matching the participant\u27s pose with an initial pose through a tracking device and software. In our case, the pose capture and skeletal model are provided by the Microsoft Kinect and its associated SDK. Second, color image, depth data, and human tracking information from the Kinect and its SDK are used to automatically extract the contour of the participant and then generate a 3D human model with skeleton. Third, using the pose and the skeletal model, we segment the contour into eight parts and then match the contour points on each segment to a corresponding anchor set associated with a 3D human template. Finally, we map the color image of the person to the 3D model as its corresponding texture map. The whole modeling process only take several seconds and the resulting human model looks like the real person. The geometry of the 3D model matches the contour of the real person, and the model has a photorealistic texture. Furthermore, the mesh of the human model is attached to the skeleton provided in the template, so the model can support programmed animations or be controlled by real people. This human control is commonly done through a literal mapping (motion capture) or a gesture-based puppetry system. Our ultimate goal is to create a mixed reality (MR) system, in which the participants can manipulate virtual objects, and in which these virtual objects can affect the participant, e.g., by restricting their mobility. This MR system prototype design motivated the work of this dissertation, since a realistic 3D human model of the participant is an essential part of implementing this vision

Long-range video motion estimation using point trajectories

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (leaves 97-104).This thesis describes a new approach to video motion estimation, in which motion is represented using a set of particles. Each particle is an image point sample with a long-duration trajectory and other properties. To optimize these particles, we measure point-based matching along the particle trajectories and distortion between the particles. The resulting motion representation is useful for a variety of applications and differs from optical flow, feature tracking, and parametric or layer-based models. We demonstrate the algorithm on challenging real-world videos that include complex scene geometry, multiple types of occlusion, regions with low texture, and non-rigid deformation.by Peter Sand.Ph.D

Bayesian Video Matting Using Learnt Image Priors

This paper presents a method inspired by natural image statistics where a second order prior is learnt that models the relationship between the spatiotemporal gradients in the image sequence and those in the alpha mattes. This is used in combination with a learnt foreground color model and a prior on the alpha distribution to help regularize the solution and greatly improve the automatic performance of the syste

Multiple View Geometry For Video Analysis And Post-production

Multiple view geometry is the foundation of an important class of computer vision techniques for simultaneous recovery of camera motion and scene structure from a set of images. There are numerous important applications in this area. Examples include video post-production, scene reconstruction, registration, surveillance, tracking, and segmentation. In video post-production, which is the topic being addressed in this dissertation, computer analysis of the motion of the camera can replace the currently used manual methods for correctly aligning an artificially inserted object in a scene. However, existing single view methods typically require multiple vanishing points, and therefore would fail when only one vanishing point is available. In addition, current multiple view techniques, making use of either epipolar geometry or trifocal tensor, do not exploit fully the properties of constant or known camera motion. Finally, there does not exist a general solution to the problem of synchronization of N video sequences of distinct general scenes captured by cameras undergoing similar ego-motions, which is the necessary step for video post-production among different input videos. This dissertation proposes several advancements that overcome these limitations. These advancements are used to develop an efficient framework for video analysis and post-production in multiple cameras. In the first part of the dissertation, the novel inter-image constraints are introduced that are particularly useful for scenes where minimal information is available. This result extends the current state-of-the-art in single view geometry techniques to situations where only one vanishing point is available. The property of constant or known camera motion is also described in this dissertation for applications such as calibration of a network of cameras in video surveillance systems, and Euclidean reconstruction from turn-table image sequences in the presence of zoom and focus. We then propose a new framework for the estimation and alignment of camera motions, including both simple (panning, tracking and zooming) and complex (e.g. hand-held) camera motions. Accuracy of these results is demonstrated by applying our approach to video post-production applications such as video cut-and-paste and shadow synthesis. As realistic image-based rendering problems, these applications require extreme accuracy in the estimation of camera geometry, the position and the orientation of the light source, and the photometric properties of the resulting cast shadows. In each case, the theoretical results are fully supported and illustrated by both numerical simulations and thorough experimentation on real data

Fehlerkaschierte Bildbasierte Darstellungsverfahren

Creating photo-realistic images has been one of the major goals in computer graphics since its early days. Instead of modeling the complexity of nature with standard modeling tools, image-based approaches aim at exploiting real-world footage directly,as they are photo-realistic by definition. A drawback of these approaches has always been that the composition or combination of different sources is a non-trivial task, often resulting in annoying visible artifacts. In this thesis we focus on different techniques to diminish visible artifacts when combining multiple images in a common image domain. The results are either novel images, when dealing with the composition task of multiple images, or novel video sequences rendered in real-time, when dealing with video footage from multiple cameras.Fotorealismus ist seit jeher eines der großen Ziele in der Computergrafik. Anstatt die Komplexität der Natur mit standardisierten Modellierungswerkzeugen nachzubauen, gehen bildbasierte Ansätze den umgekehrten Weg und verwenden reale Bildaufnahmen zur Modellierung, da diese bereits per Definition fotorealistisch sind. Ein Nachteil dieser Variante ist jedoch, dass die Komposition oder Kombination mehrerer Quellbilder eine nichttriviale Aufgabe darstellt und häufig unangenehm auffallende Artefakte im erzeugten Bild nach sich zieht. In dieser Dissertation werden verschiedene Ansätze verfolgt, um Artefakte zu verhindern oder abzuschwächen, welche durch die Komposition oder Kombination mehrerer Bilder in einer gemeinsamen Bilddomäne entstehen. Im Ergebnis liefern die vorgestellten Verfahren neue Bilder oder neue Ansichten einer Bildsammlung oder Videosequenz, je nachdem, ob die jeweilige Aufgabe die Komposition mehrerer Bilder ist oder die Kombination mehrerer Videos verschiedener Kameras darstellt