Model training method and device based on skin weight, equipment and medium

Abstract

The embodiment of the invention discloses a model training method and device based on skin weight, equipment and a medium, and the method comprises the steps: obtaining a sample wearing article model with a sample weight label, and extracting a sample skeleton structure; determining sample vertex basic features based on vertex position information and normal vector information of the model vertexes; determining a sample vertex connection relation matrix based on the vertex topological structure; performing voxelization on the sample wearing article model to obtain voxels of the sample wearing article model, and determining sample geodesic distance features based on the voxels; and obtaining a prediction weight label based on the sample vertex basic features, the sample vertex connection relation matrix, the sample geodesic distance features and the initial network model, and training the initial network model based on the prediction weight label and the sample weight label to obtain a target network model used for predicting the target skin weight. By adopting the embodiment of the invention, the accuracy of predicting the skin weight can be improved.

Description

technical field [0001] The present application relates to the field of computer technology, and in particular to a skin weight-based model training method, device, equipment and medium. Background technique [0002] At present, in the animation production process, since the wearable object model (for example, a 3D human clothing model) and the bone structure distributed in the 3D human clothing are independent of each other, in order to let the bone structure drive the 3D human clothing model to produce reasonable motion In the process of creating the 3D human clothing model, the computer device with skinning function needs to set the skinning weight between the vertices of the model and the bone structure to achieve the effect of binding the 3D human clothing model to the bone structure. [0003] It can be understood that the existing skinning algorithm can determine the distance feature corresponding to the model vertex and the bone structure by calculating the Euclidean d...

AI Technical Summary

Problems solved by technology

However, in practical applications involving curved surfaces, the Euclidean distance cannot accurately represent the distance between the bone structure and the model vertices on the 3D human clothing model, which leads to inaccurate final skin weight settings

For example, in the case of a three-dimensional human clothing model standing naturally, a certain bone structure 1 (for example, the bone structure where the elbow joint is located) may be placed close to the ribs, so the bone structure 1 determined by the computer device and the model of the area under the ribs The Euclidean distance between the vertices is small, so that the skin weight between the finally determined model vertices of the subcostal area and the bone structure 1 is relatively large, that is, the model vertices of the subcostal area are largely influenced by the elbow joint. In fact, the vertices of the model in the subcostal area should be less or even not affected by the elbow joint. In this way, when the 3D human clothing model moves with the bone structure, the 3D human clothing model may have severe deformation such as the face , interspersed and other abnormal conditions, which reduces the accuracy of the skin weight

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