系统仿真学报 ›› 2016, Vol. 28 ›› Issue (10): 2378-2385.

• 仿真建模理论与方法 • 上一篇    下一篇

基于Kinect的实时360度虚拟试衣

张晓丽, 姚俊峰, 黄萍   

  1. 厦门大学软件学院数字媒体技术研究中心,福建 厦门 361005
  • 收稿日期:2016-05-31 修回日期:2016-07-11 出版日期:2016-10-08 发布日期:2020-08-13
  • 作者简介:张晓丽(1990-),女,山东济宁,硕士,研究方向为计算机图形学、虚拟现实技术;姚俊峰(通讯作者1973-),男,山西单县,博士,教授,研究方向为数字化真人研究、虚拟现实系统开发与应用等。
  • 基金资助:
    国家自然科学基金(61174161),福建省工业科技重大专项(2013HZ0004-1)

360-Degree Virtual Fitting Based on Kinect

Zhang Xiaoli, Yao Junfeng, Huang Ping   

  1. Center for Digital Media Computing, Software School, Xiamen University, Xiamen 361005, China
  • Received:2016-05-31 Revised:2016-07-11 Online:2016-10-08 Published:2020-08-13

摘要: 很多虚拟试衣系统只研究人机交互和服装模拟,不能做到衣服模型随人体进行360度旋转。为解决该问题,提出基于Kinect运动预测的虚拟试衣改进方法。借助Kinect获取使用者身上的骨骼特征点进行实时追踪,根据得到的头部关节点信息和彩色图像,进行人脸检测,判断出使用者的前后面。对左右肩部关节点信息进行基于灰色模型的运动轨迹预测,当关节点深度坐标发生跳变时,对Kinect获得的数据进行纠正。该方法有以下优点:真实感,系统实现了360度虚拟试衣;实时性,灰色预测能快速得出预测结果,实现衣服模型随人体实时旋转。实验结果表明该3D虚拟试衣系统试衣效果良好。

关键词: Kinect, 骨骼特征点, 人脸检测, 灰色预测, 360度旋转

Abstract: A lot of virtual fitting systems only study human-computer interaction and clothing simulation, but they can’t make clothes model to rotate 360 degree along with human. To solve this problem, an improved method of virtual fitting based on Kinect using motion prediction was proposed. With the help of Kinect, the skeleton feature points of user for real-time tracking were obtained. According to the obtained information of the joint point of the head and color image, face was detected and judging the front or back of the user. The motion trajectory of the left and right shoulders’ joint points based on gray model was predicted. When the depth coordinates of joint point varied sharply, the data was corrected that Kinect obtained. The proposed method has the following advantages: Sense of reality, the system realizes the real-time 360 degree virtual fitting; Real-time, the gray forecast can predict the result quickly, which achieves real-time rotation of clothing model with the human body. Experimental results show that the 3D virtual fitting system can achieve a better fitting effect result.

Key words: Kinect, skeletal characteristic points, face detection, gray prediction, 360 degree rotation

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