Realistic behavior of control actuators is important for virtual proto-typing applications. We present a systematic approach for modeling such articulated components as described in the European Standard EN 894-3. Control actuators may have several rotational and translational degrees of freedom (DOFs), possibly with discrete lock states. During user interactions, information about the actuators' manipulation is collected and made available to the higher application layers in the form of interaction events. This allows for recording and playback of demonstrated manipulation sequences for many purposes, such as ergonomics evaluations involving virtual humans. The framework uses XML for declaration and is implemented using a freely available physics engine.

Abstract We propose a novel data‐driven animation method for the synthesis of natural looking human grasping. Motion data captured from human grasp actions is used to train a probabilistic model of the human grasp space. This model greatly reduces the high number of degrees of freedom of the human hand to a few dimensions in a continuous grasp space. The low dimensionality of the grasp space in turn allows for efficient optimization when synthesizing grasps for arbitrary objects. The method requires only a short training phase with no need for preprocessing of graphical objects for which grasps are to be synthesized. Copyright © 2008 John Wiley & Sons, Ltd.

This paper presents a comparison of various classification methods for the problem of recognizing grasp types involved in object manipulations performed with a data glove. Conventional wisdom holds that data gloves need calibration in order to obtain accurate results. However, calibration is a time-consuming process, inherently user-specific, and its results are often not perfect. In contrast, the present study aims at evaluating recognition methods that do not require prior calibration of the data glove, by using raw sensor readings as input features and mapping them directly to different categories of hand shapes. An experiment was carried out, where test persons wearing a data glove had to grasp physical objects of different shapes corresponding to the various grasp types of the Schlesinger taxonomy. The collected data was analyzed with 28 classifiers including different types of neural networks, decision trees, Bayes nets, and lazy learners. Each classifier was analyzed in six different settings, representing various application scenarios with differing generalization demands. The results of this work are twofold: (1) We show that a reasonably well to highly reliable recognition of grasp types can be achieved - depending on whether or not the glove user is among those training the classifier - even with uncalibrated data gloves. (2) We identify the best performing classification methods for recognition of various grasp types. To conclude, cumbersome calibration processes before productive usage of data gloves can be spared in many situations.

We present a novel method for virtual character animation that we call action capture. In this approach, virtual characters learn to imitate the actions of Virtual Reality (VR) users by tracking not only the users' movements but also their interactions with scene objects.Action capture builds on conventional motion capture but differs from it in that higher-level action representations are transferred rather than low-level motion data. As an advantage, the learned actions can often be naturally applied to varying situations, thus avoiding retargetting problems of motion capture. The idea of action capture is inspired by human imitation learning; related methods have been investigated for a longer time in robotics. The paper reviews the relevant literature in these areas before framing the concept of action capture in the context of VR-based character animation. We also present an example in which the actions of a VR user are transferred to a virtual worked.

Advanced VR simulation systems are composed of several components with independent and heterogeneously structured databases. To guarantee a closed and consistent world simulation, flexible and robust data exchange between these components has to be realized. This multiple database problem is well known in many distributed application domains, but it is central for VR setups composed of diverse simulation components. Particularly complicated is the exchange between object-centered and graph-based representation formats, where entity attributes may be distributed over the graph structure. This article presents an abstract declarative attribute representation concept, which handles different representation formats uniformly and enables automatic data exchange and synchronization between them. This mechanism is tailored to support the integration of a central knowledge component, which provides a uniform representation of the accumulated knowledge of the several simulation components involved. This component handles the incoming-possibly conflicting-world changes propagated by the diverse components. It becomes the central instance for process flow synchronization of several autonomous evaluation loops.