Multi-precision grid refinement method based on CNN cloth wrinkle recognition

A multi-precision, grid technology, applied in biological neural network models, instruments, calculations, etc., can solve the problems that it takes some time to identify wrinkles, the simulation effect is not as good as the simulation effect, and the overall effect is not improved, so as to avoid complexity. , fine mesh, realistic cloth effect

Active Publication Date: 2019-12-10
ZHONGBEI UNIV
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AI Technical Summary

Problems solved by technology

[0004] The above method no longer uses complex curvature calculations, but it still takes some time to identify wrinkles, an

Method used

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  • Multi-precision grid refinement method based on CNN cloth wrinkle recognition
  • Multi-precision grid refinement method based on CNN cloth wrinkle recognition
  • Multi-precision grid refinement method based on CNN cloth wrinkle recognition

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Embodiment Construction

[0061] In the following, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the embodiments of the present invention.

[0062] Examples, and all other embodiments obtained by persons of ordinary skill in the art without creative efforts, all belong to the scope of protection of the present invention.

[0063] Such as figure 1 As shown, the multi-precision mesh refinement method based on CNN cloth fold recognition includes the following steps:

[0064] Step 1. Establish a human body model, perform animation simulation, extract key animation frames and segment the folded parts, and store the segmented model in image format; the specific process and the principles to be followed are:

[0065] Step 1.1 Calculate the motion state of the cloth motion: set the initial motion state as (x 0 ,v 0 )

[0066]

[0067] A·Δv=b (2)

[0068] Among them, x is a 3n×1-dimensional state vector, v is a 3n×1-dimen...

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Abstract

The invention belongs to the technical field of computer animation and discloses a multi-precision grid refinement method based on CNN cloth wrinkle recognition, and the method comprises the steps: firstly building a human body model, carrying out animation simulation, extracting a key animation frame, carrying out the segmentation of a wrinkle part, enabling the segmented model to serve as the input of a convolutional neural network, and obtaining the recognition of wrinkles through CNN training so that refinement is carried out on the identified wrinkle part by using a pixel grid; and finally, converting the refined quadrilateral grid into a triangular grid, and carrying out cloth simulation. Compared with a traditional wrinkle recognition method based on curvature calculation, the wrinkle recognition method has the advantages that the wrinkle recognition speed is increased while accurate wrinkle recognition is guaranteed. A pixel refinement method is adopted to refine the grid, so that the grid is finer, and the simulated cloth is more vivid.

Description

technical field [0001] The invention belongs to the technical field of computer animation, and in particular relates to a multi-precision grid refinement method based on CNN cloth fold recognition. Background technique [0002] Virtual simulation technology has become more and more popular in recent years, and its application range is very wide. The VR technology that has appeared in recent years belongs to the category of virtual simulation. The game industry is also a relatively popular field, especially the animation effect of clothing. Its fidelity directly affects the effect of the entire animation. Because the cloth is a flexible material, it is easy to deform, and it is precisely because of the bending and deformation of the cloth that it will produce certain wrinkles, which affect the delicate feeling of the animation. In order to quickly identify the wrinkled parts, some studies have adopted traditional methods to identify the degree of cloth bending deformation by...

Claims

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Application Information

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IPC IPC(8): G06T13/40G06N3/04
CPCG06T13/40G06N3/045
Inventor 靳雁霞贾瑶
Owner ZHONGBEI UNIV
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