Search Results (23)

Digital restoration has become an effective way to restore traditional costumes, but there are still problems such as low modeling efficiency and difficult pattern recognition. This study focuses on Tang Dynasty ladies in the “ramming & washing silk painting”, integrating AI + 3D technology with costume culture for comprehensive restoration. The methodology includes era-specific analysis for identity and body shape determination, image-based sizing, fabric identification, color matching, and pattern restoration using AI and edge-detection algorithms. Comparative analysis with original images guides 3D modeling and AI-assisted restoration of makeup, hairstyles, and accessories. The successful restoration of the lady on the left’s costume in the “ramming & washing silk painting” proves the effectiveness of this AI + 3D method in protecting and innovating traditional clothing. Full article

Background and Objectives: Traction alopecia is a common type of hair loss that primarily results from prolonged tension in hair follicles. This condition is often associated with certain hairstyles and hair care practices that are prevalent in various cultures, especially in Africa. There have been few studies on this issue in Africa, and none have been conducted in Sudan. Therefore, we aimed to examine the prevalence and associated factors of traction alopecia in women in north Sudan. Materials and Methods: A community-based, cross-sectional study was conducted in north Sudan in December 2022. Women’s sociodemographic characteristics were assessed using a questionnaire, and hair and scalp examinations were performed. A multivariate binary analysis was performed. Results: A total of 192 women participated in the study, and 48 (25.0%) had traction alopecia. The median age of the women was 42.0 years (interquartile range: 32.0–52.0 years). In a multivariate binary analysis, a family history of women with male pattern baldness or thinning (adjusted odds ratio [AOR] = 2.96, 95% confidence interval [CI] = 1.05–8.37) and the use of hair color or chemicals (AOR = 2.98, 95% CI = 1.30–6.83) were positively associated with traction alopecia. In contrast, increasing age was inversely associated with traction alopecia (AOR = 0.96, 95% CI = 0.93–0.99). The women with traction alopecia showed characteristics such as hair breakage, hair loss with the root attached, scalp tenderness, and trichodynia. Conclusions: In north Sudan, one in four women is affected by traction alopecia. Increasing awareness of this condition, providing education on proper hair care methods, and conducting large-scale research are essential steps to prevent its occurrence. Full article

In this study, we propose a framework for synthesizing new characters by applying the features of a reference face to a source face using a diffusion model-based image editing technique. For an effective synthesis, a blank face of the sample is first generated by removing all features except the hairstyle, face shape, and skin tone. Then, facial features such as the eyes, eyebrows, nose, and mouth from the reference face are applied to the blank face of the source face. This strategy allows the creation of a new character that reflects the reference face’s features on the source face without producing unwanted artifacts or undesired blending of facial features. Full article

The purpose of this study is to explore the key roles and functions of institutions or organizations in the process of the spatial activation and industrial transformation of leisure fishing villages and to summarize this operating model. This study takes Xunpu Village, Quanzhou City, Fujian Province as the research object, which is well-known for its recreational fisheries. It mainly uses case study methods and uses on-site surveys and interviews to understand the formation time, spatial distribution, and business type information of the Zanhuawei shops in Xunpu Village. Zanhuawei, which refers to the traditional hairstyle of Xunpu women, was later applied for national intangible cultural heritage status in China. After the outbreak, the spread of Zanhuawei on the Internet inspired a large number of tourists to visit Xunpu Village to experience Zanhuawei. The study found that (1) Zanhuawei industry replaced traditional fishery and became the leading industry type in Xunpu Village. (2) The operating model led by the government and integrating public and private funds has had a stimulating effect on the revitalization of local commercial and residential space, and a large number of idle spaces have been reactivated. (3) The cooperation model between a large number of investors and local fisherwomen has completely changed the economic model of the village. Zanhuawei industry mobilized all official and media resources to promote spatial activation at various scales, including regions, settlements and commercial spaces. Full article

In 2003, the British Design Council advocated for strategic design and its management. They researched ways to create a universal framework for describing the design process and came up with the Double Diamond Model, which became a part of the universal design language. The Double Diamond Model applies to innovation and problem-solving involving divergence and convergence at four stages: Discover, Define, Develop, and Deliver. Continuous divergence, convergence, and iteration are necessary at each stage of the design process. Therefore, we reinterpreted design process methods to optimize and apply the entire design process in various fields. A case study applying the Multi-Diamond Model to users’ experiences during a hairstyling appointment was conducted to understand user needs and behavioral differences. Users’ behaviors and their understanding of hairstyling were analyzed by employing the fuzzy comprehensive evaluation method to choose hairstyles that were suitable for various face shapes at different perceived difficulty levels. Full article

The application of augmented reality (AR) in the beauty and hairdressing industry provides customers with a rich experience, especially in terms of virtual hair styling. Through virtual hair color tests, customers can quickly decide on the most suitable hair dye for them. Teenagers use multimedia communication channels to distinctively express their enjoyment of and aesthetic experiences with the interactive, emotional, and spatial aspects of AR. They can also preview diverse hair dyes and hairstyles in a virtual environment and, ultimately, select the option that suits them the most. This study applied the Stimulus–Organism–Response (S–O–R) theoretical framework and collected 337 valid samples through a SmartPLS-4-supported questionnaire survey for analysis. The results revealed that spatiality significantly influenced behavioral intention in the context of aesthetic experiences, whereas interactivity significantly influenced behavioral intention in the context of entertainment experiences. Overall, the hair dye consumption behavior of Taiwanese teenagers was positively influenced by spatiality and interactivity, and the overall model was well structured. The findings of this study can serve as a reference for businesses to develop innovative technology products that enhance consumer marketing experiences and can contribute to the future development of the hair dye segment of the fashion industry. Full article

This study introduces a virtual reality (VR)-based remote hair design training system that addresses the limitations of physical presence. By leveraging virtual environments and eliminating the need for tangible tools, this system allows hairstylists and learners to collaborate and master hair-design techniques from a distance. In this approach, a section of a user’s hair was derived from their photograph, and during the training, the most compatible 3D hair model was integrated with the user’s 3D avatar using the recommended matching procedure. This aligned hairstyle was subsequently incorporated into a 3D model for hair simulation. Further, VR HMD interaction mapping was used for hair cutting and styling. A collaborative environment for hair design has been crafted, enabling multiple participants to partake in remote hairstyle education. This system offers cost-effective training, enhances cooperative learning, and aligns with the requirements of contact-free education. In essence, this research has transformed remote hair design training with immersive VR technology. To verify the system’s accuracy, tests were conducted, and the results showcased enhancements in the matching procedure. Full article

The virtual hair styling service, which now is necessary for cosmetics companies and beauty centers, requires significant improvement efforts. In the existing technologies, the result is unnatural as the hairstyle image is serviced in the form of a ‘composite’ on the face image, image, extracts and synthesizing simple hair images. Because of complicated interactions in illumination, geometrical, and occlusions, that generate pairing among distinct areas of an image, blending features from numerous photos is extremely difficult. To compensate for the shortcomings of the current state of the art, based on GAN-Style, we address and propose an approach to image blending, specifically for the issue of visual hairstyling to increase accuracy and reproducibility, increase user convenience, increase accessibility, and minimize unnaturalness. Based on the extracted real customer image, we provide a virtual hairstyling service (Live Try-On service) that presents a new approach for image blending with maintaining details and mixing spatial features, as well as a new embedding approach-based GAN that can gradually adjust images to fit a segmentation mask, thereby proposing optimal styling and differentiated beauty tech service to users. The visual features from many images, including precise details, can be extracted using our system representation, which also enables image blending and the creation of consistent images. The Flickr-Faces-HQ Dataset (FFHQ) and the CelebA-HQ datasets, which are highly diversified, high quality datasets of human faces images, are both used by our system. In terms of the image evaluation metrics FID, PSNR, and SSIM, our system significantly outperforms the existing state of the art. Full article

Traditional GAN-based image generation networks cannot accurately and naturally fuse surrounding features in local image generation tasks, especially in hairstyle generation tasks. To this end, we propose a novel transformer-based GAN for new hairstyle generation networks. The network framework comprises two modules: Face segmentation (F) and Transformer Generative Hairstyle (TGH) modules. The F module is used for the detection of facial and hairstyle features and the extraction of global feature masks and facial feature maps. In the TGH module, we design a transformer-based GAN to generate hairstyles and fix the details of the fusion part of faces and hairstyles in the new hairstyle generation process. To verify the effectiveness of our model, CelebA-HQ (Large-scale CelebFaces Attribute) and FFHQ (Flickr-Faces-HQ) are adopted to train and test our proposed model. In the image evaluation test used, FID, PSNR, and SSIM image evaluation methods are used to test our model and compare it with other excellent image generation networks. Our proposed model is more robust in terms of test scores and real image generation. Full article

Methylcellulose and chitosan served as promising ingredients for a thermoresponsive hair styling gel after successful application in the medical industry. Both ingredients uphold the clean beauty standard without infringing on performance. By combining these two ingredients, a hair gel can be created that promises an extended hold of style once a heated external stimulus, such as a curling wand, is applied to the hair. Chitosan serves as the cationic biopolymer to adhere the gel to the hair, whereas the methylcellulose acts as the smart biopolymer to lock the desired hairstyle in place. Various ranges of chitosan and methylcellulose concentrations were explored for formulation optimization with rheology and curl drop testing. The rheology testing included a flow sweep test to understand the shear-thinning behavior of the sample as well as the effect of concentration on viscosity. Another rheology test completed was a temperature ramp test from room temperature (25 °C) to 60 °C to study the effect of heat on the various concentrations within the samples. A curl drop test was performed as well, over a 48-h period in which the different samples were applied to wet hair tresses, dried, curled, and hung vertically to see how the style held up over a long period of time with the influence of gravity. Full article

Interest in hairstyling, which is a means of expressing oneself, has increased, as has the number of people who are attempting to change their hairstyles. A considerable amount of time is required for women to change their hair back from a style that does not suit them, or for women to regrow their long hair after changing their hair to a short hairstyle that they do not like. In this paper, we propose a model combining Mask R-CNN and a generative adversarial network as a method of overlaying a new hairstyle on one’s face. Through Mask R-CNN, hairstyles and faces are more accurately separated, and new hairstyles and faces are synthesized naturally through the use of a generative adversarial network. Training was performed over a dataset that we constructed, following which the hairstyle conversion results were extracted. Thus, it is possible to determine in advance whether the hairstyle matches the face and image combined with the desired hairstyle. Experiments and evaluations using multiple metrics demonstrated that the proposed method exhibits superiority, with high-quality results, compared to other hairstyle synthesis models. Full article

In the last few years, Augmented Reality, Virtual Reality, and Artificial Intelligence (AI) have been increasingly employed in different application domains. Among them, the retail market presents the opportunity to allow people to check the appearance of accessories, makeup, hairstyle, hair color, and clothes on themselves, exploiting virtual try-on applications. In this paper, we propose an eyewear virtual try-on experience based on a framework that leverages advanced deep learning-based computer vision techniques. The virtual try-on is performed on a 3D face reconstructed from a single input image. In designing our system, we started by studying the underlying architecture, components, and their interactions. Then, we assessed and compared existing face reconstruction approaches. To this end, we performed an extensive analysis and experiments for evaluating their design, complexity, geometry reconstruction errors, and reconstructed texture quality. The experiments allowed us to select the most suitable approach for our proposed try-on framework. Our system considers actual glasses and face sizes to provide a realistic fit estimation using a markerless approach. The user interacts with the system by using a web application optimized for desktop and mobile devices. Finally, we performed a usability study that showed an above-average score of our eyewear virtual try-on application. Full article

Recent advances in depth measurement and its utilization have made point cloud processing more critical. Additionally, the human head is essential for communication, and its three-dimensional data are expected to be utilized in this regard. However, a single RGB-Depth (RGBD) camera is prone to occlusion and depth measurement failure for dark hair colors such as black hair. Recently, point cloud completion, where an entire point cloud is estimated and generated from a partial point cloud, has been studied, but only the shape is learned, rather than the completion of colored point clouds. Thus, this paper proposes a machine learning-based completion method for colored point clouds with XYZ location information and the International Commission on Illumination (CIE) LAB ($L^{*}{a}^{*}{b}^{*}$) color information. The proposed method uses the color difference between point clouds based on the Chamfer Distance (CD) or Earth Mover’s Distance (EMD) of point cloud shape evaluation as a color loss. In addition, an adversarial loss to $L^{*}{a}^{*}{b}^{*}\text{-}Depth$ images rendered from the output point cloud can improve the visual quality. The experiments examined networks trained using a colored point cloud dataset created by combining two 3D datasets: hairstyles and faces. Experimental results show that using the adversarial loss with the colored point cloud renderer in the proposed method improves the image domain’s evaluation. Full article

Skin problems are among the most common ailments on Earth. Despite its popularity, assessing it is not easy because of the complexities in skin tones, hair colors, and hairstyles. Skin disorders provide a significant public health risk across the globe. They become dangerous when they enter the invasive phase. Dermatological illnesses are a significant concern for the medical community. Because of increased pollution and poor diet, the number of individuals with skin disorders is on the rise at an alarming rate. People often overlook the early signs of skin illness. The current approach for diagnosing and treating skin conditions relies on a biopsy process examined and administered by physicians. Human assessment can be avoided with a hybrid technique, thus providing hopeful findings on time. Approaches to a thorough investigation indicate that deep learning methods might be used to construct frameworks capable of identifying diverse skin conditions. Skin and non-skin tissue must be distinguished to detect skin diseases. This research developed a skin disease classification system using MobileNetV2 and LSTM. For this system, accuracy in skin disease forecasting is the primary aim while ensuring excellent efficiency in storing complete state information for exact forecasts. Full article

Hairstyling trends among Black women fluctuate with social, cultural, and environmental pressures. Dermatologists should be aware of current trends and their associated risks in order to provide the best care to this population. In order to summarize the updated trends and associated health risks for the most common hairstyles worn by Black women, a literature review was performed. PubMed and EMBASE were used to identify articles related to hair styling practices, studies on the effects or risks of various styling practices, and magazine articles citing current styling trends among women of African descent. All hairstyles were found to have associated health risks; however, natural styles had the fewest adverse associations of all styles reviewed. Central Centrifugal Cicatricial Alopecia (CCCA) is the most cited hair disorder in this population, possibly linked to both chemical relaxants and traction styles. Additional studies are needed to further establish causality between these styles and CCCA. Additionally, while acceptance of natural hairstyles is on the rise, there is more work to be done throughout society to help protect and encourage women who choose to wear Afrocentric styles. Dermatologists should be well versed in these hairstyles and ready to lend appropriate advice to patients when it is requested. Full article