We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
基于机器学习的静态三维仿真衣袖模型构建.
- Authors
张惠
- Abstract
Aiming at the problem that the existing virtual fitting software cannot simulate in real time, a virtual simulation method for static sleeves based On machine learning was proposed* Firstly, 20 2D sleeve samples were evenly divided according to the variation law of armholes, sleeve heights and sleeve fats* Six kinds of fabrics were combined to produce 120 3D simulation sleeve samples consistent with the real fabric drape index in CL03D. Then, a method of transforming triangular meshes into regular quadrilaterals was proposed > and the irregular triangular meshes of the sleeve model were transformed into regular quadrilateral meshes through UV transfer* Finally, two machine learning algorithms random forest RF and extreme gradient boosting XGBoost y were selected to fit and Optimize* The results show that the MAE value of the vertex coordinate prediction value of the XGBoost algorithm model is 1. 81 mm, which is 0. 51 mm lower than that of the RF model. The MAPE value is 11.03%, which is 2* 51% lower than that of the RF model* It takes 0. 389 s to predict a set of data on average. The overall performance is better than the RF model* When the new sleeve template data is input, the XGBoost algorithm model can quickly output the corresponding three-dimensional sleeve mesh vertex coordinates, which provides a reference for the study of real-time simulation of static virtual clothing*.
- Subjects
BOOSTING algorithms; MACHINE learning; QUADRILATERALS; SLEEVES; DRAPERIES; ALGORITHMS
- Publication
Wool Textile Journal, 2023, Vol 51, Issue 2, p89
- ISSN
1003-1456
- Publication type
Article
- DOI
10.19333/j.mfkj.20220604109