Looking Beyond Appearances: Synthetic Training Data for Deep CNNs in Re-identification

Re-identification is generally carried out by encoding the appearance of a subject in terms of outfit, suggesting scenarios where people do not change their attire. In this paper we overcome this restriction, by proposing a framework based on a deep convolutional neural network, SOMAnet, that additionally models other discriminative aspects, namely, structural attributes of the human figure (e.g. height, obesity, gender). Our method is unique in many respects. First, SOMAnet is based on the Inception architecture, departing from the usual siamese framework. This spares expensive data preparation (pairing images across cameras) and allows the understanding of what the network learned. Second, and most notably, the training data consists of a synthetic 100K instance dataset, SOMAset, created by photorealistic human body generation software. Synthetic data represents a good compromise between realistic imagery, usually not required in re-identification since surveillance cameras capture low-resolution silhouettes, and complete control of the samples, which is useful in order to customize the data w.r.t. the surveillance scenario at-hand, e.g. ethnicity. SOMAnet, trained on SOMAset and fine-tuned on recent re-identification benchmarks, outperforms all competitors, matching subjects even with different apparel. The combination of synthetic data with Inception architectures opens up new research avenues in re-identification.Comment: 14 page

Improving the Apparel Virtual Size Fitting Prediction under Psychographic Characteristics and 3D Body Measurements Using Artificial Neural Network

3D virtual simulation prototyping software combined with computer-aided manufacturing systems are widely used and are becoming essential in the fashion industry in the earlier stages of the product development process for apparel design. These technologies streamline the garment product fitting procedures, as well as improve the supply chain environmentally, socially, and economically by eliminating large volumes of redundant samples. Issues of non-standardized selection on garment sizing, ease allowance, and size of 3D avatar for creating 3D garments have been addressed by many researchers. Understanding the relationship between body dimensions, ease allowance, and apparel sizes before adopting virtual garment simulation is fundamental for satisfying high customer demands in the apparel industry. However, designers find difficulties providing the appropriate garment fit for customers without fully understanding the motivation and emotions of customers’ fitting preferences in a virtual world. The main purpose of this study is to investigate apparel sizes for virtual fitting, particularly looking at garment ease with consideration of body dimensions and the psychographic characteristics of subjects. In order to develop a virtual garment fitting prediction model, an artificial neural network (ANN) was applied. We recruited 50 subjects between the ages of 18 and 35 years old to conduct 3D body scans and a questionnaire survey for physical and psychological segmentation, as well as fitting preferences evaluation through co-design operations on virtual garment simulation using a commercial software called Optitex. The results from the study demonstrate that ANN is effective in modeling the non-linear relationship between pattern measurements, psychological characteristics, and body measurements. This new approach and the proposed method of virtual garment fitting model prediction on garment sizes using an Artificial Neural Network (ANN) is significant in prediction accuracy. The project also achieves the concept of mass customization and customer orientation and generates new size-fitting data that can bring a new level of end-user satisfaction

Classification of geometrical objects by integrating currents and functional data analysis. An application to a 3D database of Spanish child population

This paper focuses on the application of Discriminant Analysis to a set of geometrical objects (bodies) characterized by currents. A current is a relevant mathematical object to model geometrical data, like hypersurfaces, through integration of vector fields along them. As a consequence of the choice of a vector-valued Reproducing Kernel Hilbert Space (RKHS) as a test space to integrate hypersurfaces, it is possible to consider that hypersurfaces are embedded in this Hilbert space. This embedding enables us to consider classification algorithms of geometrical objects. A method to apply Functional Discriminant Analysis in the obtained vector-valued RKHS is given. This method is based on the eigenfunction decomposition of the kernel. So, the novelty of this paper is the reformulation of a size and shape classification problem in Functional Data Analysis terms using the theory of currents and vector-valued RKHS. This approach is applied to a 3D database obtained from an anthropometric survey of the Spanish child population with a potential application to online sales of children's wear

Consumer Culture and Purchase Intentions towards Fashion Apparel

This study examines the effectiveness of different fashion marketing strategies and analyzes of the consumer behavior in a cross-section of demographic settings in reference to fashion apparel retailing. This paper also discusses the marketing competencies of fashion apparel brands and retailers in reference to brand image, promotions, and externalmarket knowledge. The study examines the determinants of consumer behavior and their impact on purchase intentions towards fashion apparel. The results reveal that sociocultural and personality related factors induce the purchase intentions among consumers. One of the contributions that this research extends is the debate about the converging economic, cognitive and brand related factors to induce purchase intentions. Fashion loving consumers typically patronage multi-channel retail outlets, designer brands, and invest time and cost towards an advantageous product search. The results of the study show a positive effect of store and brand preferences on developing purchase intentions for fashion apparel among consumers.Consumer behavior, purchase intention, socio-cultural values, designer brands, store brands, fashion apparel, brand promotion, personalization, fashion retailing, psychographic drivers

Consumers\u27 Preferred Body Scanning Technology: A Comparison

Poor fit of apparel products has been troublesome for both the consumers and manufacturers for many years. The acquisition of a correct set of body measurements is crucially important for achieving proper fitting apparel. Three-dimensional body scanning technology has been recognized as a promising alternative to the traditional measuring tape method of obtaining body measurements. Three-dimensional body scanners are quick, efficient, highly reproducible, and largely free of error related to human intervention. The purpose of this study was to investigate consumers’ preferred type of body scanning technology. Three types of body scanners (traditional body scanner, suit-based body scanner, and mobile-based body scanner) were compared using the Technology Acceptance Model (Davis, 1989) as the theoretical framework. Consumers’ perception on usefulness and ease of use were compared among the three scanner types. An online survey was administered using Qualtrics® software for data collection. Data included 382 responses, out of which only 220 were valid. Data was analyzed using SAS® software to test formulated hypotheses. Findings indicated that participants’ perceived usefulness did not vary across the three types of body scanners, but the mobile-based body scanner was perceived to be easier to use than the traditional body scanner. The suit-based body scanner was perceived to be easier to use by men. Gender did not have any significant effect in the preference of the traditional and the suit-based body scanner, but gender was a significant source of variation in preference of the mobile-based body scanner

A kernel regression procedure in the 3D shape space with an application to online sales of children's wear

Shape regression is of key importance in many scienti c elds. In this paper, we focus on the case where the shape of an object is represented by a con- guration matrix of landmarks. It is well known that this shape space has a nite-dimensional Riemannian manifold structure (non-Euclidean) which makes it di cult to work with. Papers about regression on this space are scarce in the literature. The majority of them are restricted to the case of a single explanatory variable, usually time or age, and many of them work in the approximated tangent space. In this paper we adapt the general method for kernel regression analysis in manifold-valued data proposed by Davis et al (2007) to the three-dimensional case of Kendall's shape space and generalize it to multiple explanatory variables. We also propose bootstrap con dence intervals for prediction. A simulation study is carried out to check the goodness of the procedure, and nally it is applied to a 3D database obtained from an anthropometric survey of the Spanish child population with a potential application to online sales of children's wear

Anthropometric clothing measurements from 3D body scans

We propose a full processing pipeline to acquire anthropometric measurements from 3D measurements. The first stage of our pipeline is a commercial point cloud scanner. In the second stage, a pre-defined body model is fitted to the captured point cloud. We have generated one male and one female model from the SMPL library. The fitting process is based on non-rigid Iterative Closest Point (ICP) algorithm that minimizes overall energy of point distance and local stiffness energy terms. In the third stage, we measure multiple circumference paths on the fitted model surface and use a non-linear regressor to provide the final estimates of anthropometric measurements. We scanned 194 male and 181 female subjects and the proposed pipeline provides mean absolute errors from 2.5 mm to 16.0 mm depending on the anthropometric measurement