Deformable grabbing interaction method under virtual reality environment

Abstract

The invention provides a deformable grabbing interaction method under a virtual reality environment. The method specifically includes the steps of creating and adopting two layers of skin for bindinga hand skeleton to form a virtual 3D hand model, creating a smooth directional distance field of virtual 3D objects through a smoothing filter operator, and then adopting the distance field for driving a three-dimensional virtual hand contact surface to be deformed. The defects that interaction of a virtual reality hand grip is unnatural and hands which are not identified by force feedback glovesare likely to penetrate through the objects are overcome, and by using the two layers of skin for binding the virtual 3D hand skeleton and adopting equidistant surfaces for creating the virtual 3D objects, interaction is achieved; moreover, the smoothing operator filters out high-frequency contact burr to make the hand model always attached to the surface of a grabbed object, and therefore the natural interaction experience is improved.

Description

technical field [0001] The invention relates to the field of computer graphics modeling and virtual reality human-computer gesture interaction, in particular to a deformable hand grasping interaction method in a virtual reality environment. Background technique [0002] With the development and popularization of virtual reality technology, vision-based gesture recognition technology has become one of the research hotspots in the field of gesture interaction. Gesture interaction is to use technologies such as computer graphics to identify static and dynamic information of human hands and convert them into commands to operate equipment, making up for the unnatural shortcomings of human-computer interaction methods such as mouse, keyboard and touch screen. However, the current domestic and foreign research on this aspect has certain defects, such as: use the method of maximum likelihood criterion Hausdorff distance and multi-resolution search strategy to better recognize letter...

AI Technical Summary

Problems solved by technology

Its advantage is that it can directly obtain some static and dynamic information about the hand. The disadvantage is that it encapsulates the impact of the closedness of the recognition method and process itself and the limitations of the information it provides. It is difficult to directly estimate the posture of the hand completely through these information. With the help of the following two vision-based human hand pose estimation methods: 1. Based on the appearance, the state of the human hand is estimated by establishing a mapping from the 2D image feature space to the 3D human hand pose space through machine learning; 2. Based on the model, the 3D model of the human hand is projected to the 2D image space, and correct the estimated pose parameters in the 3D model through feature comparison and data estimation

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