Motion Editing with Data Glove

Wai-Chun Lam, Feng Zou, Taku Komura

City University of Hong Kong 83 Tat Chee Ave Kowloon, Hong Kong

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Summary:

This paper describes a method to edit captured human motion data with a data glove.

Motion editing is frequently used to generate mew motion data from existing captured data. This procedure can be applied to characters with different body sizes using retargeting. Overlapping and blending motions is achieved with motion wrapping. These methods are traditionally based on keyframe postures, where the editor determines when the posture must be edited. The changes are then determined with inverse kinematics. This approach has the following drawbacks: In order to determine the effect of a change the motion has to be replayed. Drastic changes need the insertion of several keyframes. Keyframe editing is a static editing method whereas motion is obviously dynamic.

A new dynamic editing method using a data glove has been therefore developed. First the real human motion data that is to be used for the basic motion is prepared. The animator then mimics the motion that is viewed to create a mapping function between the hand gestures and the real human motion data. New motions not present in the original data can then be created from hand gestures alone. This method maps finger movements to the actions of the whole body and is also able to function in real time for virtual reality, gaming etc.

A P5 data glove is used where the index and middle fingers correspond to the legs and shoulders of the collected human movement data. Matching is then made by synchronizing the tips and pits of the motion curves. This stage can be repeated if required to improve the mapping function. As the degrees of movement of the human body are much greater than those of the fingers, individual finger movements are mapped to generalized coordinates for the body. The arm motions are conjugate with opposite leg actions. During the capture and mapping stages, Fourier series expansion is used to eliminate noise and high frequency data. Additionally, the domain of the mapping function can be scaled to cope with new hand gestures outside the original domain.

Evaluation & Results:

An ordinary walking motion was captured for the model data. The animator was asked to mimic this action in order for the mapping function to be generated. The animator was then asked to execute a hopping zigzag running stride. The most prominent features noted in the resulting animation were the absence of lateral body tilt and foot contact with the ground. This indicated the need to extract specific constraints from the original data and preserve them in the newly generated motion. In its current form, for the newly generated motion to be realistic it has to have a similar topographic structure to the original data. As a next step, a more detailed joint matching approach between hand and body will be explored.

Discussion:

The use of a dynamic control system in place of a static one when dealing with a dynamic system is logically very sound. However, a walking finger puppet will only be able to directly influence the legs of the human model. All other dynamics will have to be slaved to the legs or independently controlled.

In order to understand the results more thoroughly a more rigorous comparison needs to be presented. A comparison to a traditional static keyframe approach would be useful to identify advantages and shortcomings.

However, a very interesting approach. To be able to control and modify a complex motion with a simple and intuitive action surely has many applications.