Development of Intelligent Garment Coordination and Virtual Try-on System for Fashion Retailing with RFID Technology

Improving customer experience has become a more and more important role in enhancing customer service in fashion retailing business. In this study, a kind of intelligent garment coordination and try-on system for fashion retailing was proposed. Radio Frequency Identification （RFID） technology was used to identify customer and garment item automatically. The intelligent procedure for garment coordination recommendation using Artificial Neural Network （ANN） was developed to imitate fashion designers＇ decision-making on garment coordination. Virtual try-on algorithm based on the customer＇s 2D/mago was accomplished using imagewarping technique. The system architecture and the software framework were also described. The results show that the ＇system is a practical and useful application for fashion retailers.

Virtual Dressing Room Application with Virtual Human Using Kinect Sensor

This study proposes a real time 3D virtual model controll and a virtual dressing room application to enable users to try virtual garments and shoes on in front of a virtual mirror. A virtual representation of the user appears in a virtual changing room and the user＇s hand motions select the clothes from a list on the screen. Afterwards, the selected virtual clothes appear on a humanoid model in the virtual mirror. For the purpose of aligning the 3D garments and shoes with the model, 3D locations of the joints are used for positioning, scaling and rotating. By using our developed algorithm, small, medium, large or xlarge garment size is selected automatically and this information is shown on the screen. Then, we apply skin color detection to handle the unwanted occlusions between the user and the model. To create a more realistic effect, the system takes into account different images of the clothes according to different human poses and movements. Optional mirror selection buttons make it possible to have multiple viewing angles on the model. Additionally, we developed an algorithm for matching up all motions between the model and garments. In this study, we benefit from the Microsoft Kinect SDK （software development kit） in order to follow the user＇s movements, coordinate the suitable clothe try-ons and provide depth sort effect to the human body and clothes. In order to use the rapid calculation attributes of game engines, we used unity 3D game engine.

眼镜零售业计算机虚拟试戴方法研究

眼镜行业信息化建设主要涉及的两个方向是眼镜店销售管理系统和基于虚拟试戴技术的销售系统。计算机虚拟试戴方法研究是眼镜行业信息化研究的重点。该研究主要运用了计算机图形图像处理学的知识。实现了人脸与镜架按照真实比例的试戴,以及染色镜片效果的演示,是一种适合制作眼镜行业销售系统的方法。

一种基于增强现实的自适应在线虚拟眼镜试戴新技术

针对在线虚拟眼镜试戴系统中头部转动时试戴效果不佳的问题,研究了一种基于增强现实的自适应在线虚拟眼镜试戴技术。选用单目摄像头作为输入设备,采集人脸信息,建立基于支持向量机的人脸分类器,进行局部特征提取和人脸对齐。通过头部姿态估计,解决了眼镜与头部姿态的自适应问题。将上述技术应用到移动设备,开发了一个在移动平台上应用的增强现实虚拟眼镜试戴系统。实验结果证明:当人脸处于移动设备前方30~50 cm时,系统能够自动识别用户的脸部数据,正视图试戴正确率为100%;当人体头部旋转、戴眼镜、戴帽子,以及光照不足时,匹配准确率会有所降低,但总体的结果依然表现出较好的趋势。

High fidelity virtual try-on network via semantic adaptation and distributed componentization

Image-based virtual try-on systems have significant commercial value in online garment shopping.However,prior methods fail to appropriately handle details,so are defective in maintaining the original appearance of organizational items including arms,the neck,and in-shop garments.We propose a novel high fidelity virtual try-on network to generate realistic results.Specifically,a distributed pipeline is used for simultaneous generation of organizational items.First,the in-shop garment is warped using thin plate splines(TPS)to give a coarse shape reference,and then a corresponding target semantic map is generated,which can adaptively respond to the distribution of different items triggered by different garments.Second,organizational items are componentized separately using our novel semantic map-based image adjustment network(SMIAN)to avoid interference between body parts.Finally,all components are integrated to generatethe overall result by SMIAN.A priori dual-modalinformation is incorporated in the tail layers of SMIAN to improve the convergence rate of the network.Experiments demonstrate that the proposed method can retain better details of condition information than current methods.Our method achieves convincing quantitative and qualitative results on existing benchmark datasets.

ARCosmetics:a real-time augmented reality cosmetics try-on system

A virtual cosmetics try-on system provides a realistic try-on experience for consumers and helps them efficiently choose suitable cosmetics.In this article,we propose a real-time augmented reality virtual cosmetics try-on system for smartphones(ARCosmetics),taking speed,accuracy,and stability into consideration at each step to ensure a better user experience.A novel and very fast face tracking method utilizes the face detection box and the average position of facial landmarks to estimate the faces in continuous frames.A dynamic weight Wing loss is introduced to assign a dynamic weight to every landmark by the estimated error during training.It balances the attention between small,medium,and large range error and thus increases the accuracy and robustness.We also designed a weighted average method to utilize the information of the adjacent frame for landmark refinement,guaranteeing the stability of the generated landmarks.Extensive experiments conducted on a large 106-point facial landmark dataset and the 300-VW dataset demonstrate the superior performance of the proposed method compared to other state-of-the-art methods.We also conducted user satisfaction studies further to verify the efficiency and effectiveness of our ARCosmetics system.