Data processing method and device, and computer readable storage medium

Abstract

The invention discloses a data processing method and device, and a computer readable storage medium. The data processing method comprises steps of for each joint point in a plurality of joint points in skeletal animation, joint motion data of the joint points being obtained, and the joint motion data indicating a motion sequence of the joint points changing along with time; for each data point inthe joint motion data of the joint point, determining an instantaneous change rate of a motion data value of the data point, and determining a filter parameter corresponding to the data point; and foreach data point in the articulation data of the articulation point, adaptively filtering the data point by using the determined filter parameter. According to the method, the different filter parameters corresponding to each data point are determined based on the instantaneous change rate of the motion data value of each data point in the joint motion data, so adaptive filtering of the joint motion data is realized, the noise can be smoothed, and details of the action can be retained.

Description

technical field [0001] The present invention relates to the field of computer graphics processing, in particular to a data processing method, device and computer-readable storage medium. Background technique [0002] Skeletal skinning animation, also called skeletal animation, can create a variety of lifelike animated characters by using skeletal animation technology. Update the position of the joints in real time according to the translation or rotation data of the bone joints, so that the whole character can move. However, the movement of joint points usually has jitter, and animators need to perform noise reduction processing on these movement curves to make the movement of the entire character smoother. [0003] In the prior art, ordinary Butterworth low-pass filter is usually used to denoise the motion curve. For ordinary Butterworth low-pass filter, using a unique cut-off frequency in a signal may not be able to reduce noise and A good balance has been achieved in te...

AI Technical Summary

Problems solved by technology

Although wavelet analysis can distinguish the frequency contained in the signal at different times, the effect obtained by different basis functions may be quite different, and it is necessary to continue to try

Moreover, when wavelet analysis filters the frequency domain signal and returns to the time domain signal, there may be a discontinuous phenomenon such as sawtooth, and the discontinuous signal is accompanied by jitter with a high probability.

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