Research On Method Of 3D Personalized Human Body Modeling Based On Photos And Neural Network

With the rapid popularization of the computer and network technology, MTM (Made to Measure) has become an important research direction for garment design. 3D (three-dimensional) human body modeling has an important role in MTM, and is the basis for 3D garment CAD system. If a 3D mannequin can truly reflect actual human size and shape depends on the modeling methods. Therefore, to stady and find new methods of modeling has important theoretical and applied significance.Based on current research status of human body modeling, this paper presents a method of 3D human personalized body modeling based on photo and neural network. 2D (two-dimensional) size information of human body is got by extraction of feature region and parameters based on user image. 3D cross-section information is got by generation of feature curve of human body based on neural network. 3D standard mannequins are generated by search for similar body from scanned human body database and by human surface reconstruction. Personalized mannequins are got through human deformation driven by feature size and curve of human body.A 2D human body feature region and feature parameters extraction method based on user image is proposed. On the basis of binary images of the user photo got through image pre-processing, a method of how to distinguish each feature region of the human body is given. This provides a basis for contour matching user body and similar body. According to the modeling method and the reference method, the way to recognize feature points and calculate feature sizes of human body deducted from image pixel is introduced. This method provides 2D size information for the generation of feature curve and 3D human body modeling.A general method for human body feature curves automatically generating based on neural network is proposed. The technique of training sample data acquisition and the model of neural network are given. By making use of real human body data, the training samples of the neck curve, bust curve, waist curve and hip curve are obtained. The weight and the feature curve of the neck, bust, waist and hip can be got respectively after training. It makes up the defect that 3D cross-section information can not be got from user photo. The error analysis is done and the results show that the method can approach user human body feature curve. Only with the thickness, size of girth, and other 2D information, cross-section information of the curve can be obtained.A method for searching for similar body from scanned human body database and human surface reconstruction is proposed. Firstly, scanned human body database is established, then making use of size, feature point and contour triple match, similar body best matched user body is searched for from scanned human body database, finally, according to the human surface reconstruction technique, a 3D standard mannequin is generated.A human body deformation method driven by feature size and curve is also proposed. According to size information obtained from user photo and cross-section information got from human body feature curve, 3D standard mannequin is deformed to personalized mannequin driven by feature size and curve. The above research constitutes a complete system for personalized human body modeling.A 3D personalized human body modeling module has been developed by exploiting the algorithms presented in this thesis and has been integrated into a garment design system named LookStailorX(LSX). Using the LSX, many examples including the technique for feature parameters extraction, human body feature curve generation, scanned body reconstruction, body deformation, combination drive, and personalized human body modeling are given.