Similarity, Retrieval, and Classification of Motion Capture Data

Three-dimensional motion capture data is a digital representation of the complex spatio-temporal structure of human motion. Mocap data is widely used for the synthesis of realistic computer-generated characters in data-driven computer animation and also plays an important role in motion analysis tasks such as activity recognition. Both for efficiency and cost reasons, methods for the reuse of large collections of motion clips are gaining in importance in the field of computer animation. Here, an active field of research is the application of morphing and blending techniques for the creation of new, realistic motions from prerecorded motion clips. This requires the identification and extraction of logically related motions scattered within some data set. Such content-based retrieval of motion capture data, which is a central topic of this thesis, constitutes a difficult problem due to possible spatio-temporal deformations between logically related motions. Recent approaches to motion retrieval apply techniques such as dynamic time warping, which, however, are not applicable to large data sets due to their quadratic space and time complexity. In our approach, we introduce various kinds of relational features describing boolean geometric relations between specified body points and show how these features induce a temporal segmentation of motion capture data streams. By incorporating spatio-temporal invariance into the relational features and induced segments, we are able to adopt indexing methods allowing for flexible and efficient content-based retrieval in large motion capture databases. As a further application of relational motion features, a new method for fully automatic motion classification and retrieval is presented. We introduce the concept of motion templates (MTs), by which the spatio-temporal characteristics of an entire motion class can be learned from training data, yielding an explicit, compact matrix representation. The resulting class MT has a direct, semantic interpretation, and it can be manually edited, mixed, combined with other MTs, extended, and restricted. Furthermore, a class MT exhibits the characteristic as well as the variational aspects of the underlying motion class at a semantically high level. Classification is then performed by comparing a set of precomputed class MTs with unknown motion data and labeling matching portions with the respective motion class label. Here, the crucial point is that the variational (hence uncharacteristic) motion aspects encoded in the class MT are automatically masked out in the comparison, which can be thought of as locally adaptive feature selection

Motion templates for automatic classification and retrieval of motion capture data

This paper presents new methods for automatic classification and retrieval of motion capture data facilitating the identification of logically related motions scattered in some database. As the main ingredient, we introduce the concept of motion templates (MTs), by which the essence of an entire class of logically related motions can be captured in an explicit and semantically interpretable matrix representation. The key property of MTs is that the variable aspects of a motion class can be automatically masked out in the comparison with unknown motion data. This facilitates robust and efficient motion retrieval even in the presence of large spatio-temporal variations. Furthermore, we describe how to learn an MT for a specific motion class from a given set of training motions. In our extensive experiments, which are based on several hours of motion data, MTs proved to be a powerful concept for motion annotation and retrieval, yielding accurate results even for highly variable motion classes such as cartwheels, lying down, or throwing motions

Efficient content-based retrieval of motion capture data

The reuse of human motion capture data to create new, realistic motions by applying morphing and blending techniques has become an important issue in computer animation. This requires the identification and extraction of logically related motions scattered within some data set. Such content-based retrieval of motion capture data, which is the topic of this paper, constitutes a difficult and timeconsuming problem due to significant spatio-temporal variations between logically related motions. In our approach, we introduce various kinds of qualitative features describing geometric relations between specified body points of a pose and show how these features induce a time segmentation of motion capture data streams. By incorporating spatio-temporal invariance into the geometric features and adaptive segments, we are able to adopt efficient indexing methods allowing for flexible and efficient content-based retrieval and browsing in huge motion capture databases. Furthermore, we obtain an efficient preprocessing method substantially accelerating the cost-intensive classical dynamic time warping techniques for the time alignment of logically similar motion data streams. We present experimental results on a test data set of more than one million frames, corresponding to 180 minutes of motion. The linearity of our indexing algorithms guarantees the scalability of our results to much larger data sets

Efficient Indexing And Retrieval Of Motion Capture Data Based on Adaptive Segmentation

In this paper we propose a unified approach to efficient indexing and content-based retrieval of human motion capture data as used in data-driven computer animation or computer vision. Opposed to other data formats such as music or video, the kinematic chain (a kind of human skeleton) as underlying model of motion capture data allows to introduce qualitative boolean features describing geometric relations of specified points of the body. In combination with such geometric features, we introduce the concept of adaptive temporal segmentation of motion data streams, which accounts for the spatio-temporal invariance needed to identify logically related motions. This allows us to adopt efficient indexing and fault-tolerant retrieval methods such as fuzzy search. Here, the crucial point is that our adaptive segmentation not only adjusts to the granularity of the feature function but also to the fuzziness of the query. We present experimental results on a test data set of more than one million frames corresponding to 180 minutes of motion capture data. The linearity of our indexing algorithms guarantees the scalability of our results to much larger data sets

Documentation Mocap Database HDM05

In the past two decades, motion capture (mocap) systems have been developed that allow to track and record human motions at high spatial and temporal resolutions. The resulting motion capture data is used to analyze human motions in fields such as sports sciences and biometrics (person identification), and to synthesize realistic motion sequences in datadriven computer animation. Such applications require efficient methods and tools for the automatic analysis, synthesis and classification of motion capture data, which constitutes an active research area with many yet unsolved problems. Even though there is a rapidly growing corpus of motion capture data, the academic research community still lacks publicly available motion data, as supplied by [4], that can be freely used for systematic research on motion analysis, synthesis, and classification. Furthermore, a common dataset of annotated and well-documented motion capture data would be extremely valuable to the research community in view of an objective comparison and evaluation of the achieved research results. It is the objective of our motion capture database HDM05 1 to supply free motion capture data for research purposes. HDM05 contains more than tree hours of systematically recorded and well-documented motio