Try-on Methods Research Articles

FICE: Text-conditioned fashion-image editing with guided GAN inversion

Fashion-image editing is a challenging computer-vision task where the goal is to incorporate selected apparel into a given input image. Most existing techniques, known as Virtual Try-On methods, deal with this task by first selecting an example image of the desired apparel and then transferring the clothing onto the target person. Conversely, in this paper, we consider editing fashion images with text descriptions. Such an approach has several advantages over example-based virtual try-on techniques: (i) it does not require an image of the target fashion item, and (ii) it allows the expression of a wide variety of visual concepts through the use of natural language. Existing image-editing methods that work with language inputs are heavily constrained by their requirement for training sets with rich attribute annotations or they are only able to handle simple text descriptions. We address these constraints by proposing a novel text-conditioned editing model called FICE (Fashion Image CLIP Editing) that is capable of handling a wide variety of diverse text descriptions to guide the editing procedure. Specifically, with FICE, we extend the common GAN-inversion process by including semantic, pose-related, and image-level constraints when generating images. We leverage the capabilities of the CLIP model to enforce the text-provided semantics, due to its impressive image–text association capabilities. We furthermore propose a latent-code regularization technique that provides the means to better control the fidelity of the synthesized images. We validate the FICE through rigorous experiments on a combination of VITON images and Fashion-Gen text descriptions and in comparison with several state-of-the-art, text-conditioned, image-editing approaches. Experimental results demonstrate that the FICE generates very realistic fashion images and leads to better editing than existing, competing approaches. The source code is publicly available from: https://github.com/MartinPernus/FICE.

A Markov Chain approach for video-based virtual try-on with denoising diffusion generative adversarial network

Video-based virtual try-ons have attracted unprecedented attention owing to the development of e-commerce. However, this problem is very challenging because of the arbitrary poses of persons and the demand for temporary consistency of frames, particularly when attempting to synthesize high-quality virtual try-on videos using single images. Specifically, there are two key challenges. 1) The existing video-based virtual try-on methods are based on generative adversarial networks (GAN), which are limited by unstable training and a lack of realism in generated details. 2) The explicit building of stronger constraints of generated frames, which aims to increase the coherence of generated videos. To address these challenges, this study proposed a novel framework, Extended Markov Chain Based Denoising Diffusion Generative Adversarial Network (EMC-DDGAN), which was derived from a denoising diffusion GAN, which is a diffusion model with efficient sampling. Moreover, we proposed an extended Markov chain that used a diffusion model to synthesize frames via sequential generation. With a carefully designed network and learning objects, the proposed approach achieved outstanding performance on public datasets. Rigorous experiments demonstrated that EMC-DDGAN could synthesize higher-quality videos compared to other state-of-the-art methods and validated the effectiveness of the proposed approach.

A progressive distillation network for practical image-based virtual try-on

The image-based virtual try-on technology aims to match in-store clothing to person's image wearing clothes. Previous methods require a large amount of input information for each try-on result, which is not practical, and slight deviations in input information can cause a large number of artifacts in the try-on results. In recent years, researchers have paid attention to parsing-free virtual try-on methods, and a groundbreaking work used knowledge distillation techniques to eliminate the redundant input. This method uses the parsing-based virtual try-on model as the supervision information to train a student network model, which can generate try-on results without extra input. However, due to the huge knowledge gap between the teacher-student networks, direct distillation makes it difficult for the student network to fully simulate the teacher network. To solve this problem, we propose a progressive distillation scheme for image-based virtual try-on called PD-VTON, using an assistant network to alleviate this huge knowledge gap. Our method can generate more realistic and reasonable try-on results without requiring extra body parsing information or body pose information. Specifically, unlike existing distillation-based parsing-free virtual try-on methods, we adopt an assistant-supported progressive distillation network to alleviate the insufficient learning caused by the large knowledge gap and design an Adaptive Choose Teacher (ACT) module to optimize the distillation. Moreover, we introduce a novel cross attention-based stitching structure when generating try-on images, aiming to better constrain the alignment between the high-level semantic features of person image and warped clothing image, and use a transformer-assisted generator to generate the results. Finally, extensive experimental evaluations demonstrate the unique advantages of our method.

Arbitrary Virtual Try-on Network: Characteristics Preservation and Tradeoff between Body and Clothing

Deep learning based virtual try-on system has achieved some encouraging progress recently, but there still remain several big challenges that need to be solved, such as trying on arbitrary clothes of all types, trying on the clothes from one category to another and generating image-realistic results with few artifacts. To handle this issue, we in this article first collect a new dataset with all types of clothes, i.e., tops, bottoms, and whole clothes, each one has multiple categories with rich information of clothing characteristics such as patterns, logos, and other details. Based on this dataset, we then propose the Arbitrary Virtual Try-On Network (AVTON) that is utilized for all-type clothes, which can synthesize realistic try-on images by preserving and trading off characteristics of the target clothes and the reference person. Our approach includes three modules: (1) Limbs Prediction Module, which is utilized for predicting the human body parts by preserving the characteristics of the reference person. This is especially good for handling cross-category try-on task (e.g., long sleeves ↔ short sleeves or long pants ↔ skirts), where the exposed arms or legs with the skin colors and details can be reasonably predicted; (2) Improved Geometric Matching Module, which is designed to warp clothes according to the geometry of the target person. We improve the TPS based warping method with a compactly supported radial function (Wendland’s Ψ-function); (3) Trade-Off Fusion Module, which is to tradeoff the characteristics of the warped clothes and the reference person. This module is to make the generated try-on images look more natural and realistic based on a fine-tune symmetry of the network structure. Extensive simulations are conducted and our approach can achieve better performance compared with the state-of-the-art virtual try-on methods.

Self-supervised feature matched virtual try-on

Abstract Virtual try-on is a technology that enables users to preview the effect of wearing a target garment without wearing the actual garment. However, existing image-based virtual try-on methods often require additional human parsing or segmentation operations to generate intermediate representations required for garment deformation and texture fusion. These operations not only increase the computational complexity and memory consumption, but also limit the real-time and portability of virtual try-on. Additionally, inaccurate parsing results can lead to misleading final generated images. To overcome these challenges, we propose a self-supervised feature matched virtual try-on network, which can directly generate high-quality try-on results from human body images and target clothing images without any additional input. Specifically, we design an optical flow warp module, which focuses on the optical flow changes between the person image and the clothing image to achieve accurate clothing alignment and deformation. Furthermore, a feature refine warp module is designed to enhance the features of the extracted optical flow information and the original character segmentation and analysis operations, reducing the influence of background clutter features on the content, and ensuring that the wrinkles and deformation of the replacement clothes are close to the original clothes. The feature match module is developed to calculate the feature matching loss of the converted clothing and the generated results of the teacher network and the student network, and the corresponding knowledge is distilled and passed to the student network to assist in self-supervised training. We conduct experiments on the VITON dataset and show that our model can generate high quality and high resolution, and our proposed method outperforms the state-of-the-art virtual try-on methods both qualitatively and quantitatively.

ST-VTON: Self-supervised vision transformer for image-based virtual try-on

Virtual try-on techniques are in the spotlight due to the promise of enhancing the user experience of online shopping in the apparel industry. Among these technologies, image-based virtual try-on methods have received more attention due to their efficiency and low cost. These methods commonly warp the specific clothing item to the desired shape and then fuse it with the target person image. However, existing approaches demand the laborious paired dataset of clothes and persons. While recent works alleviate the dependence on paired datasets by transferring clothing from one person to another, the pose information is still a rigid demand to guide the synthesis. To solve the above-mentioned problems, we propose ST-VTON, a two-stage self-supervised try-on approach that can synthesize photo-realistic try-on results with only person images and a standard ViT structure. Specifically, we first utilize massive person and clothing images to pre-train a powerful ViT encoder following the MAE approach to enhance the generalization ability and accelerate model training. Secondly, we propose the Clothing Recovery Module (CRM) to reconstruct in-shop clothes from person images and use the paired generated clothing and corresponding person images to train a new ViT decoder while optimizing the pre-trained encoder. Noted that, the CRM, as an independent module, can be used to effectively expand existing paired try-on datasets and enhance other try-on approaches in terms of training data. Extensive experiments conducted on the VITON dataset and UnPaired virtual Try-on (UPT) dataset with both paired and unpaired approaches demonstrate that try-on results rendered by our model can match or even outperform supervised methods. • We propose a novel ViT-backed try-on method with lightweight preprocessing and a standard ViT without elaborate modification. • .We introduce a two-stage self-supervised training method which can expand paired try-on datasets from separate person images. • Our model can achieve competitive results for both paired try-on and unpaired clothing transfer.

Two‐dimensional virtual try‐on algorithm and application research for personalized dressing

To reduce the cost of virtual try-on, a method of image deformation by body part size is proposed for the traditional two-dimensional virtual try-on method, which is challenging to represent the personalized characteristics of the body size of the fitting subject. On the basis of the input information of the user's body size, the method can generate a fitting effect that shows the user's characteristics with the corresponding clothes. The image segmentation algorithm is used to snap out the garment from the background garment image, and then the size and position of the garment are adjusted according to the dressing position of the standard mannequin image and fit the mannequin image. The final mesh with dense vertices is generated using surface subdivision. The experimental results show that this method can show good results in user personalized try-on.

Toward multi-category garments virtual try-on method by coarse to fine TPS deformation

Toward multi-category garments virtual try-on method by coarse to fine TPS deformation

A Comparative Study of Virtual Footwear Try-On Applications in Virtual and Augmented Reality

Abstract Virtual try-on technology (VTO) in virtual reality (VR) and augmented reality (AR) has been developed for years to create novel shopping experiences for users by allowing them to virtually wear fashion products. Compared to garments or facial accessories, fewer studies have focused on virtual footwear try-on, regardless of user study or technical development. Thus, it is necessary to examine the effectiveness of existing VTO applications on the user's affective responses. In this study, we compared the user experience of three different footwear try-on methods (real, VR, and AR) with both physiological and psychological measures. Subjects conducted a try-on experiment on different pairs of sneakers. Each subject’s gaze trajectory was recorded using an eye tracker and analyzed to show his/her visual attention in each method. Afterward, the subjects completed questionnaires to assess the sense of presence, usability, and the user experience score for the try-on processes, and subsequently attended a think-aloud procedure to express their thoughts. The analysis results of the collected data showed that the user experience produced by the VR and AR try-on is not comparable to that of the real environment. The results also revealed factors that negatively affect the quality of the user's interaction with the processes. These findings may provide insights into further improvements in VTO technology.

Virtual Try-on Network With Attribute Transformation and Local Rendering

A virtual try-on network has gradually become a popular topic in recent years. It aims to transfer images of in-shop clothes onto the image of a target person. Owing to the diversity of clothing attributes, developing an image-based virtual try-on network is a complicated task for computers to perform and requires significant effort. Existing methods are unsatisfactory as they cannot preserve the characteristics of the clothes or the target person's identity well, thereby affecting the perception of the generated images; therefore, further research is required. To address this problem, we propose a novel try-on method that combines attribute transformation and local rendering. First, we employ pixel-level semantic segmentation to identify the try-on area and provide implementation conditions for local rendering. Second, we construct a learnable attribute transformation module to complete the try-on task for different attributes. Third, we use a learnable clothing warping module to fit the pose and figure of the target person well and establish a novel loss function, called modified style loss (M-SL), to handle clothes with rich details. Finally, we adopt a local rendering strategy, using which only renders the clothing area to ensure that the details of the non-target area are not lost. Extensive experiments are performed to test our method. The results demonstrate that our method outperforms other state-of-the-art methods.

UVIRT-Unsupervised Virtual Try-on Using Disentangled Clothing and Person Features.

Virtual Try-on is the ability to realistically superimpose clothing onto a target person. Due to its importance to the multi-billion dollar e-commerce industry, the problem has received significant attention in recent years. To date, most virtual try-on methods have been supervised approaches, namely using annotated data, such as clothes parsing semantic segmentation masks and paired images. These approaches incur a very high cost in annotation. Even existing weakly-supervised virtual try-on methods still use annotated data or pre-trained networks as auxiliary information and the costs of the annotation are still significantly high. Plus, the strategy using pre-trained networks is not appropriate in the practical scenarios due to latency. In this paper we propose Unsupervised VIRtual Try-on using disentangled representation (UVIRT). After UVIRT extracts a clothes and a person feature from a person image and a clothes image respectively, it exchanges a clothes and a person feature. Finally, UVIRT achieve virtual try-on. This is all achieved in an unsupervised manner so UVIRT has the advantage that it does not require any annotated data, pre-trained networks nor even category labels. In the experiments, we qualitatively and quantitatively compare between supervised methods and our UVIRT method on the MPV dataset (which has paired images) and on a Consumer-to-Consumer (C2C) marketplace dataset (which has unpaired images). As a result, UVIRT outperform the supervised method on the C2C marketplace dataset, and achieve comparable results on the MPV dataset, which has paired images in comparison with the conventional supervised method.

A generic method of wearable items virtual try-on

Virtual try-on synthesizes garments for the target bodies in 2D/3D domains. Even though existing virtual try-on methods focus on redressing garments, the virtual try-on hair, shoes and wearable accessories are still under-reached. In this paper, we present the first general method for virtual try-ons that is fully automatic and suitable for many items including garments, hair, shoes, watches, necklaces, hats, and so on. Starting with the pre-defined wearable items on a reference human body model, an automatic method is proposed to deform the reference body mesh to fit a target body for obtaining dense triangle correspondences. Then, an improved fit metric is used to represent the interaction between wearable items and the body. For the next step, with the help of triangle correspondences and the fit metric, the wearable items can be fast and efficiently inferred by the shape and posture of the targeted body. Extensive experimental results show that, besides automation and efficiency, the proposed method can be easily extended to implement the dynamic try-on by applying rigging and importing motion capture data, being able to handle both tight and loose garments, and even multi-layer clothing.

SP-VITON: shape-preserving image-based virtual try-on network

Image-based virtual try-on networks for changing the outfit of a person in an image with the desired clothes of another image have attracted increasing research interests. Previous work try to extract a clothing-agnostic person representation from the original person image and then synthesize it with the given clothes image through a try-on network. However, their body shape representation just downsamples the clothed body segmentation to a low resolution, which is too coarse and still contains noises of original clothes and may result in unrealistic artifacts. Correspondingly, we propose an SP-VITON (Shape-Preserving VIrtual Try-On Network) to keep the user’s original body shape while getting rid of the original clothes. Firstly, we augment the shape variety of the dataset and estimate the 2D shape under clothes of the person using DensePose. Then a try-on network is trained with the augmented dataset and new shape representation. Experiment results show our improvements for applying to various shapes and clothes types of the input person image, compared with the state-of-the-art image-based try-on methods.

Virtual Glasses Try-on Based on Large Pose Estimation

Virtual glasses try-on displays the front and side effects of the glasses to the user by loading the virtual glasses model onto the user’s photos or videos. Currently, virtual glasses try-on methods are either inaccurate or depending on sophisticated equipment, and cannot be applied to a large-scale rotation situation. In this paper, we design and implement a novel virtual glasses try-on method based on large-scale head pose estimation, using 3D face reconstruction and pose estimation techniques, to estimate the continuous pose angle of a face, and then rotates the glasses model synchronously, in order to realize the accurate matching of glasses and face. Compared with other methods, the proposed method does not depend on sophisticated equipment, and also can be applied to the large-scale rotation situation.

Dimensions of concern over telecommunications privacy in the United States

Abstract Analyses of attitudes and concerns about privacy from a national survey sample of 1,532 adult Americans in 1988 revealed several separate dimensions. A modification of Tryon's method of clustering variables (third‐order correlations) yielded groups of variables whose meanings were much more interpretable than they had been when factor analysis was initially used. We tested the validity of the clustering by a highly comprehensive system of item analyses. The main findings were that (1) respondent concerns over telephone privacy were not related to other aspects of privacy, (2) interest in devices like Caller ID that enable recipients to know the number of the caller were not related to other aspects of telephone privacy, and (3) general privacy concerns under conditions where the respondent and the other party know each other were independent of those conditions where they do not know each other. Implications for telecommunications policy and new services are discussed.

Circularity of Vocational Interests: Spherical Analysis of VIP Items

Roe and Holland have suggested that interest categories are ordered in a circular configuration and felt that this model would be helpful in explaining changes in vocational behavior. Spatial configuration analysis has confirmed that the internal structural relationships of scales from several different interest inventories are similar and do conform to the circular ordering proposed by Roe and Holland. The purpose of this study was twofold: (a) to acquaint readers with Tryon's spherical analysis and show that spatial configuration analysis is a special case of Tryon's method and (b) to determine if items from ACT's Vocational Interest Profile conform to previous results obtained from a spatial configuration analysis of the VIP Scales. Spherical analysis confirmed the ordering effect of items for males, but a U-shaped configuration instead of a circular one was found. Results for females were difficult to interpret and suggest that women did not respond to the items in the same manner as males. Different theories of career choice may prove necessary for males and females. Results of previous spatial configuration analysis need to be reanalyzed at an item level to determine if previous findings with scales are due to an artifact.

Teachers, Students, and Ideas

Publisher Summary This chapter presents a personal account by Wendell Bell regarding teachers, students, and ideas. There are several distinctive ways of doing sociology. It is important to keep in consideration that brief intellectual encounters sometimes can be as important as enduring relationships or as those that are consciously recognized as significant. Social area analysis is basically a framework for the analysis of urban neighborhoods. It is linked both to a theory of social change and a description of long-term social trends as broad contexts within which basic variables for the construction of a typology of modern urban neighborhoods are developed. The chapter also presents a comparison between the Shevky typology with the Tryon method of urban analysis. The urban mode of life may be characterized by secondary contacts, but such contacts were not so much substituted for as added to primary contacts.

Sex-role preferences and adjustment in early adolescents.

Authorities dealing with the personality development and adjustment of children are continually stressing the major importance of achieving the role appropriate to one's sex. While there are a number of studies dealing with the masculinity-femininity dimension as it relates to adult adjustment, there is relatively little research examining sex-role preferences and adjustment in childhood. This lack of research has been acutely felt in the area of adolescent development. The present study was an effort at exploring the possible relations which might exist between sex-appropriate role preferences in early adolescents and several indices of adjust nent. The relations with which the author was principally concerned related to some variables readily observable in the school setting. Seward (12) has written that in early adolescence certain changes in sex-role expectation are introduced which strain the growing child's integrative ability. Many of the changes would appear to be related to the confusion which is being generated by a lack of clarity in the female role. For example, in a study of boys and girls at two age levels (12 and 15 years), Tryon (13) found that sexrole values for girls undergo some marked changes in the direction of greater masculine role preferences. Tuddenham (14) used Tryon's method with a younger group and concluded that girls have much more flexible role choices than boys and that masculine traits increase in value in girls with increasing age. Rabban (ii) noted that both working class and middle class boys ranging in age from 3 to 8 are more clearly aware of sex-appropriate behavior than are girls. In a study of junior high school youngsters, Helper (io) found that boys who follow the role model provided by their father gain