Exploring the use of skeletal tracking for cheaper motion graphs and on-set decision making in Free-Viewpoint Video production

In free-viewpoint video (FVV), the motion and surface appearance of a real-world performance is captured as an animated mesh. While this technology can produce high-fidelity recreations of actors, the required 3D reconstruction step has substantial processing demands. This means FVV experiences are currently expensive to produce, and the processing delay means on-set decisions are hampered by a lack of feedback. This work explores the possibility of using RGB-camera-based skeletal tracking to reduce the amount of content that must be 3D reconstructed, as well as aiding on-set decision making. One particularly relevant application is in the construction of Motion Graphs, where state-of-the-art techniques require large amounts of content to be 3D reconstructed before a graph can be built, resulting in large amounts of wasted processing effort. Here, we propose the use of skeletons to assess which clips of FVV content to process, resulting in substantial cost savings with a limited impact on performance accuracy. Additionally, we explore how this technique could be utilised on set to reduce the possibility of requiring expensive reshoots

Similarity Clustering of Dimensions for an Enhanced Visualization of Multidimensional Data

The order and arrangement of dimensions (variates) is crucial for the effectiveness of a large number of visualization techniques such as parallel coordinates, scatterplots, recursive pattern, and many others. In this paper, we describe a systematic approach to arrange the dimensions according to their similarity. The basic idea is to rearrange the data dimensions such that dimensions showing a similar behavior are positioned next to each other. For the similarity clustering of dimensions we need to define similarity measures which determine the partial or global similarity of dimensions. We then consider the problem of finding an optimal one- or two-dimensional arrangement of the dimensions based on their similarity. Theoretical considerations show that both, the one- and the two-dimensional arrangement problem are surprisingly hard problems, i.e. they are NPcomplete. Our solution of the problem is therefore based on heuristic algorithms. An empirical evaluation using a number of different visualization techniques shows the high impact of our similarity clustering of dimensions on the visualization results

Towards an Effective Cooperation of the Computer and the User for Classification

: Decision trees have been successfully used for the task of classification. However, state-of-the-art algorithms do not incorporate the user in the tree construction process. This paper presents a new user-centered approach to this process where the user and the computer can both contribute their strengths: the user provides domain knowledge and evaluates intermediate results of the algorithm, the computer automatically creates patterns satisfying user constraints and generates appropriate visualizations of these patterns. In this cooperative approach, domain knowledge of the user can direct the search of the algorithm. Additionally, by providing adequate data and knowledge visualizations, the pattern recognition capabilities of the human can be used to increase the effectivity of decision tree construction. Furthermore, the user gets a deeper understanding of the decision tree than just obtaining it as a result of an algorithm. To achieve the intended level of cooperation, we introdu..

Circle Segments : A Technique for Visually Exploring Large Multidimensional Data Sets

In this paper, we describe a novel technique for visualizing large amounts of high-dimensional data, called circle segments . The technique uses one colored pixel per data value and can therefore be classified as a pixel-per-value technique [Kei 96]. The basic idea of the circle segments visualization technique is to display the data dimensions as segments of a circle. If the data consists of k dimensions, the circle is partitioned into k segments, each representing one data dimension. Inside the segments, the data values belonging to one dimension are arranged from the center of the circle to the outside in a back and forth manner orthogonal to the line that halves the segment. Our first results show that the circle segment technique is very powerful for visualizing large amounts of data, providing more expressive visualizations than other wellknown techniques such as the recursive pattern technique and traditional line graphs

Recursive Pattern : A Technique for Visualizing Very Large Amounts of Data

An important goal of visualization technology is to support the exploration and analysis of very large amounts of data. In this paper, we propose a new visualization technique called recursive pattern which has been developed for visualizing large amounts of multidimensional data. The technique is based on a generic recursive scheme which generalizes a wide range of pixel-oriented arrangements for displaying large data sets. By instantiating the technique with adequate data- and application-dependent parameters, the user may largely influence the structure of the resulting visualizations. Since the technique uses one pixel for presenting each data value, the amount of data which can be displayed is only limited by the resolution of current display technology and by the limitations of human perceptibility. Beside describing the basic idea of the recursive pattern technique, we provide several examples of useful parameter settings for the various recursion levels. We further show that our recursive pattern technique is particularly advantageous for the large class of data sets which have a natural order according to one dimension (e.g. time series data). We demonstrate the usefulness of our technique by using a stock market application