At Tehran's Amirkabir University, authorities installed facial recognition software at its entrance gate to also find women not wearing the hijab, the report said. Surveillance cameras on Iran's major roads are also being used to search for uncovered women. Investigators also said they obtained the "Nazer" mobile phone app offered by Iranian police, which allows "vetted" members of the public and the police to report on uncovered women in vehicles, including ambulances, buses, metro cars and taxis. "Users may add the location, date, time and the licence plate number of the vehicle in which the alleged mandatory hijab infraction occurred, which then'flags' the vehicle online, alerting the police," the report said. According to the report, a text message is then sent to the registered owner of the vehicle, warning them they had been found in violation of the mandatory hijab laws.

The rapid evolution of the fashion industry increasingly intersects with technological advancements, particularly through the integration of generative AI. This study introduces a novel generative pipeline designed to transform the fashion design process by employing latent diffusion models. Utilizing ControlNet and LoRA fine-tuning, our approach generates high-quality images from multimodal inputs such as text and sketches. We leverage and enhance state-of-the-art virtual try-on datasets, including Multimodal Dress Code and VITON-HD, by integrating sketch data. Our evaluation, utilizing metrics like FID, CLIP Score, and KID, demonstrates that our model significantly outperforms traditional stable diffusion models. The results not only highlight the effectiveness of our model in generating fashion-appropriate outputs but also underscore the potential of diffusion models in revolutionizing fashion design workflows. This research paves the way for more interactive, personalized, and technologically enriched methodologies in fashion design and representation, bridging the gap between creative vision and practical application.

Image-based virtual try-on strives to transfer the appearance of a clothing item onto the image of a target person. Prior work focuses mainly on upper-body clothes (e.g. t-shirts, shirts, and tops) and neglects full-body or lower-body items. This shortcoming arises from a main factor: current publicly available datasets for image-based virtual try-on do not account for this variety, thus limiting progress in the field. To address this deficiency, we introduce Dress Code, which contains images of multi-category clothes. Dress Code is more than 3x larger than publicly available datasets for image-based virtual try-on and features high-resolution paired images (1024x768) with front-view, full-body reference models. To generate HD try-on images with high visual quality and rich in details, we propose to learn fine-grained discriminating features. Specifically, we leverage a semantic-aware discriminator that makes predictions at pixel-level instead of image- or patch-level. Extensive experimental evaluation demonstrates that the proposed approach surpasses the baselines and state-of-the-art competitors in terms of visual quality and quantitative results. The Dress Code dataset is publicly available at https://github.com/aimagelab/dress-code.

Actress and freelance writer Yumi Ishikawa, whose campaign against mandatory high heels in the workplace saw broad public support, has stepped up her fight against strict corporate dress codes in Japan to include a ban at some firms on women wearing glasses. Ishikawa shot to prominence this year with her drive against Japanese office culture, in which high heels are seen as near-obligatory when job-hunting or working in the office. Known by the hashtag #KuToo -- a play on words from the Japanese word "kutsu," meaning "shoes," and "kutsuu," meaning "pain" -- the campaign was recognized Monday for having generated one of the buzzwords of the year. Ishikawa's latest petition to relax the rules, delivered to the labor ministry on Tuesday, has attracted more than 31,000 signatures. "The root cause of the problem is that (there are companies) that have rules for women only -- such as a ban on wearing glasses or a requirement to wear make-up," the 32-year-old told reporters.

Actress and freelance writer Yumi Ishikawa, whose campaign against mandatory high heels in the workplace won broad public support, has stepped up her fight against strict corporate dress codes in Japan -- including a ban at some firms on women wearing glasses. Ishikawa shot to prominence this year with her drive against Japanese office culture, in which high heels are seen as near-obligatory when job-hunting or working in the office. Known by the hashtag #KuToo -- a play on words from the Japanese word "kutsu", meaning "shoes," and "kutsuu," meaning "pain" -- the campaign was chosen on Monday as one of the buzzwords of the year. Ishikawa's latest petition to relax the rules, delivered to the labor ministry on Tuesday, has attracted more than 31,000 signatures. "The root cause of the problem is that (there are companies) that have rules for women only -- such as a ban on wearing glasses or a requirement to wear make-up," the 32-year-old told reporters.