<i>VB2</i>

Gobbetti, Enrico; Balaguer, Jean-Francis; Thalmann, Daniel

doi:10.1145/168642.168659

Cited by 20 publications

(1 citation statement)

References 20 publications

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“…Many types of classifiers could be used for the learning and recognition task. For example in VB2 [14], feature vectors are extracted from the raw sensor data, and multi-layer perceptron networks [15] are used to approximate the functions that map these vectors to their respective classes.…”

Section: Hand Gesture Recognitionmentioning

confidence: 99%

Virtual Reality Software and Technology

Magnenat‐Thalmann¹,

Thalmann²

1994

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Section: Hand Gesture Recognitionmentioning

confidence: 99%

Virtual Reality Software and Technology

Magnenat‐Thalmann¹,

Thalmann²

1994

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Motor functions in the VLNET Body-Centered Networked Virtual Environment

et al. 1996

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In order to enhance the sense of presence within networked Virtual Environments it is important to increase the quality of physical or social interaction of participants with each other and with the environment. We beleive that this increase of quality can be achieved by realistic modeling and animation of the human body representing the participant, together with the natural behaviors of the objects in the environment. The representation of self as a realistic-looking virtual human body with natural movement helps participants not only to perceive each other and thus feel together, but also to interact with the environment in a straightforward and natural fashion. This property is further enhanced if the objects in the environment behave in a natural, expected way. In the Virtual Life Network (VLNET) system we use realistic human body representation together with a set of motor functions giving behaviors to actors and objects to reach the mentioned goals.

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A Multi-sensor Approach for Grasping and 3D Interaction

Huang¹,

Boulic²,

Thalmann³

et al. 1995

Computer Graphics

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In this paper, we propose a multi-sensor based method of automatic grasping motion control for multiple synthetic actors. Despite the fact that it is described and implemented in our specific model, the method is general and can be applicable to other models. A heuristic method is defined to decide the different grasping strategies from object geometry, hand geometry and observation of real grasping. Inverse kinematics can derive the final posture of the arms in order to bring the hands around the object. Multi-sensor object detection decides the finger contact points on the object and determine their position and orientation. Then, a group of polynomials derived from Euler-Lagrange equation is used to interpolate between the initial and final arm postures resulting in a more realistic real-time motion than linear interpolation. We also present 3D interactive grasping examples involving multiple synthetic actors.

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VB2

Cited by 20 publications

References 20 publications

Virtual Reality Software and Technology

Virtual Reality Software and Technology

Motor functions in the VLNET Body-Centered Networked Virtual Environment

A Multi-sensor Approach for Grasping and 3D Interaction

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