Image based human body rendering via regression & MRF energy minimization

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A machine learning method for synthesising human images is explored to create new images without relying on 3D modelling. Machine learning allows the creation of new images through prediction from existing data based on the use of training images. In the present study, image synthesis is performed at two levels: contour and pixel. A class of learning-based methods is formulated to create object contours from the training image for the synthetic image that allow pixel synthesis within the contours in the second level. The methods rely on applying robust object descriptions, dynamic learning models after appropriate motion segmentation, and machine learning-based frameworks. Image-based human image synthesis using machine learning is a research focus that has recently gained considerable attention in the field of computer graphics. It makes use of techniques from image/motion analysis in computer vision. The problem lies in the estimation of methods for image-based object configuration (i.e. segmentation, contour outline). Using the results of these analysis methods as bases, the research adopts the machine learning approach, in which human images are synthesised by executing the synthesis of contour and pixels through the learning from training image. Firstly, thesis shows how an accurate silhouette is distilled using developed background subtraction for accuracy and efficiency. The traditional vector machine approach is used to avoid ambiguities within the regression process. Images can be represented as a class of accurate and efficient vectors for single images as well as sequences. Secondly, the framework is explored using a unique view of machine learning methods, i.e., support vector regression (SVR), to obtain the convergence result of vectors for contour allocation. The changing relationship between the synthetic image and the training image is expressed as a vector and represented in functions. Finally, a pixel synthesis is performed based on belief propagation. This thesis proposes a novel image-based rendering method for colour image synthesis using SVR and belief propagation for generalisation to enable the prediction of contour and colour information from input colour images. The methods rely on using appropriately defined and robust input colour images, optimising the input contour images within a sparse SVR framework. Firstly, the thesis shows how contour can effectively and efficiently be predicted from small numbers of input contour images. In addition, the thesis exploits the sparse properties of SVR efficiency, and makes use of SVR to estimate regression function. The image-based rendering method employed in this study enables contour synthesis for the prediction of small numbers of input source images. This procedure avoids the use of complex models and geometry information. Secondly, the method used for human body contour colouring is extended to define eight differently connected pixels, and construct a link distance field via the belief propagation method. The link distance, which acts as the message in propagation, is transformed by improving the low-envelope method in fast distance transform. Finally, the methodology is tested by considering human facial and human body clothing information. The accuracy of the test results for the human body model confirms the efficiency of the proposed method

Wahrnehmungsbasierte Bildinterpolation

We present a perception-based method for image interpolation, aiming for perceptually convincing transitions between real-world images. Without 3D geometry or scene motion, perception-based image interpolation enables smooth viewpoint navigation across space and time. We show how global visual effects can be created from a collection of unsynchronized, uncalibrated images. A user study confirms the perceptual quality of the proposed image interpolation approach

Optimizing Apparent Display Resolution Enhancement for Arbitrary Videos

Display resolution is frequently exceeded by available image resolution. Recently, apparent display resolution enhancement techniques (ADRE) have demonstrated how characteristics of the human visual system can be exploited to provide super-resolution on high refresh rate displays. In this paper we address the problem of generalizing the apparent display resolution enhancement technique to conventional videos of arbitrary content. We propose an optimization-based approach to continuously translate the video frames in such a way that the added motion enables apparent resolution enhancement for the salient image region. The optimization takes the optimal velocity, smoothness and similarity into account to compute an appropriate trajectory. Additionally, we provide an intuitive user interface which allows to guide the algorithm interactively and preserve important compositions within the video. We present a user study evaluating apparent rendering quality and demonstrate versatility of our method on a variety of general test scenes.Aktuelle Kameras sind in der Lage, Videos mit sehr hoher Auflösung aufzunehmen (> 4K Pixel). Monitore, Fernseher und Projektoren haben jedoch meist eine deutlich niedrigere Auflösung (FullHD). Bei der Darstellung hochaufgelöster Videos auf diesen Geräten gehen durch das nötige Herrunterrechnen der Videodaten feine Details verloren, z.B. Haare oder die Pigmentierung von Oberflächenmaterialien. Es wird ein Verfahren präsentiert, welches die Darstellung eines beliebigen Videos mit einer Auflösung ermöglicht, die perzeptuell höher ist als die Auflösung des Ausgabegerätes

Enhanced dynamic reflectometry for relightable free-viewpoint video

Free-Viewpoint Video of Human Actors allows photo- realistic rendering of real-world people under novel viewing conditions. Dynamic Reflectometry extends the concept of free-view point video and allows rendering in addition under novel lighting conditions. In this work, we present an enhanced method for capturing human shape and motion as well as dynamic surface reflectance properties from a sparse set of input video streams. We augment our initial method for model-based relightable free-viewpoint video in several ways. Firstly, a single-skin mesh is introduced for the continuous appearance of the model. Moreover an algorithm to detect and compensate lateral shifting of textiles in order to improve temporal texture registration is presented. Finally, a structured resampling approach is introduced which enables reliable estimation of spatially varying surface reflectance despite a static recording setup. The new algorithm ingredients along with the Relightable 3D Video framework enables us to realistically reproduce the appearance of animated virtual actors under different lighting conditions, as well as to interchange surface attributes among different people, e.g. for virtual dressing. Our contribution can be used to create 3D renditions of real-world people under arbitrary novel lighting conditions on standard graphics hardware

Exploring Sparse, Unstructured Video Collections of Places

The abundance of mobile devices and digital cameras with video capture makes it easy to obtain large collections of video clips that contain the same location, environment, or event. However, such an unstructured collection is difficult to comprehend and explore. We propose a system that analyses collections of unstructured but related video data to create a Videoscape: a data structure that enables interactive exploration of video collections by visually navigating — spatially and/or temporally — between different clips. We automatically identify transition opportunities, or portals. From these portals, we construct the Videoscape, a graph whose edges are video clips and whose nodes are portals between clips. Now structured, the videos can be interactively explored by walking the graph or by geographic map. Given this system, we gauge preference for different video transition styles in a user study, and generate heuristics that automatically choose an appropriate transition style. We evaluate our system using three further user studies, which allows us to conclude that Videoscapes provides significant benefits over related methods. Our system leads to previously unseen ways of interactive spatio-temporal exploration of casually captured videos, and we demonstrate this on several video collections

Image based human body rendering via regression & MRF energy minimization

A machine learning method for synthesising human images is explored to create new images without relying on 3D modelling. Machine learning allows the creation of new images through prediction from existing data based on the use of training images. In the present study, image synthesis is performed at two levels: contour and pixel. A class of learning-based methods is formulated to create object contours from the training image for the synthetic image that allow pixel synthesis within the contours in the second level. The methods rely on applying robust object descriptions, dynamic learning models after appropriate motion segmentation, and machine learning-based frameworks. Image-based human image synthesis using machine learning is a research focus that has recently gained considerable attention in the field of computer graphics. It makes use of techniques from image/motion analysis in computer vision. The problem lies in the estimation of methods for image-based object configuration (i.e. segmentation, contour outline). Using the results of these analysis methods as bases, the research adopts the machine learning approach, in which human images are synthesised by executing the synthesis of contour and pixels through the learning from training image. Firstly, thesis shows how an accurate silhouette is distilled using developed background subtraction for accuracy and efficiency. The traditional vector machine approach is used to avoid ambiguities within the regression process. Images can be represented as a class of accurate and efficient vectors for single images as well as sequences. Secondly, the framework is explored using a unique view of machine learning methods, i.e., support vector regression (SVR), to obtain the convergence result of vectors for contour allocation. The changing relationship between the synthetic image and the training image is expressed as a vector and represented in functions. Finally, a pixel synthesis is performed based on belief propagation. This thesis proposes a novel image-based rendering method for colour image synthesis using SVR and belief propagation for generalisation to enable the prediction of contour and colour information from input colour images. The methods rely on using appropriately defined and robust input colour images, optimising the input contour images within a sparse SVR framework. Firstly, the thesis shows how contour can effectively and efficiently be predicted from small numbers of input contour images. In addition, the thesis exploits the sparse properties of SVR efficiency, and makes use of SVR to estimate regression function. The image-based rendering method employed in this study enables contour synthesis for the prediction of small numbers of input source images. This procedure avoids the use of complex models and geometry information. Secondly, the method used for human body contour colouring is extended to define eight differently connected pixels, and construct a link distance field via the belief propagation method. The link distance, which acts as the message in propagation, is transformed by improving the low-envelope method in fast distance transform. Finally, the methodology is tested by considering human facial and human body clothing information. The accuracy of the test results for the human body model confirms the efficiency of the proposed method.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Analysis and Synthesis of Interactive Video Sprites

In this thesis, we explore how video, an extremely compelling medium that is traditionally consumed passively, can be transformed into interactive experiences and what is preventing content creators from using it for this purpose. Film captures extremely rich and dynamic information but, due to the sheer amount of data and the drastic change in content appearance over time, it is problematic to work with. Content creators are willing to invest time and effort to design and capture video so why not for manipulating and interacting with it? We hypothesize that people can help and be helped by automatic video processing and synthesis algorithms when they are given the right tools. Computer games are a very popular interactive media where players engage with dynamic content in compelling and intuitive ways. The first contribution of this thesis is an in-depth exploration of the modes of interaction that enable game-like video experiences. Through active discussions with game developers, we identify both how to assist content creators and how their creation can be dynamically interacted with by players. We present concepts, explore algorithms and design tools that together enable interactive video experiences. Our findings concerning processing videos and interacting with filmed content come together in this thesis' second major contribution. We present a new medium of expression where video elements can be looped, merged and triggered interactively. Static-camera videos are converted into loopable sequences that can be controlled in real time in response to simple end-user requests. We present novel algorithms and interactive tools that enable our new medium of expression. Our human-in-the-loop system gives the user progressively more creative control over the video content as they invest more effort and artists help us evaluate it. Monocular, static-camera videos are a good fit for looping algorithms but they have been limited to two-dimensional applications as pixels are reshuffled in space and time on the image plane. The final contribution of this thesis breaks through this barrier by allowing users to interact with filmed objects in a three-dimensional manner. Our novel object tracking algorithm extends existing 2D bounding box trackers with 3D information, such as a well-fitting bounding volume, which in turn enables a new breed of interactive video experiences. The filmed content becomes a three-dimensional playground as users are free to move the virtual camera or the tracked objects and see them from novel viewpoints

INTERACT 2015 Adjunct Proceedings. 15th IFIP TC.13 International Conference on Human-Computer Interaction 14-18 September 2015, Bamberg, Germany

INTERACT is among the world’s top conferences in Human-Computer Interaction. Starting with the first INTERACT conference in 1990, this conference series has been organised under the aegis of the Technical Committee 13 on Human-Computer Interaction of the UNESCO International Federation for Information Processing (IFIP). This committee aims at developing the science and technology of the interaction between humans and computing devices. The 15th IFIP TC.13 International Conference on Human-Computer Interaction - INTERACT 2015 took place from 14 to 18 September 2015 in Bamberg, Germany. The theme of INTERACT 2015 was "Connection.Tradition.Innovation". This volume presents the Adjunct Proceedings - it contains the position papers for the students of the Doctoral Consortium as well as the position papers of the participants of the various workshops