In this paper, we propose a novel garment-centric outpainting (GCO) framework based on the latent diffusion model (LDM) for fine-grained controllable apparel showcase image generation. The proposed framework aims at customizing a fashion model wearing a given garment via text prompts and facial images. Different from existing methods, our framework takes a garment image segmented from a dressed mannequin or a person as the input, eliminating the need for learning cloth deformation and ensuring faithful preservation of garment details. The proposed framework consists of two stages. In the first stage, we introduce a garment-adaptive pose prediction model that generates diverse poses given the garment. Then, in the next stage, we generate apparel showcase images, conditioned on the garment and the predicted poses, along with specified text prompts and facial images. Notably, a multi-scale appearance customization module (MS-ACM) is designed to allow both overall and fine-grained text-based control over the generated model's appearance. Moreover, we leverage a lightweight feature fusion operation without introducing any extra encoders or modules to integrate multiple conditions, which is more efficient. Extensive experiments validate the superior performance of our framework compared to state-of-the-art methods.

This site is not a substitute or definitive to replace physicians' care. Not only will how clothing is made evolve, according to those involved with the art and operations of fashion, perhaps the way it's presented will also continue to evolve. Will there be major popularity with AI fashion models and figures, whether virtually or on real-life runways, that will assist us in completing our latest appearance? Will AI become an entire replacement for human fashion models? It's probably not likely that AI will completely replace human models or standard human experiences, at least not immediately, but as society and technologies evolve, the acceptability of further developments and the concerns toward sustainability may continue to create an array of new approaches, so who's really certain what's to come?

Soon, the model showcasing your online clothing purchases may not have actually made the pose in the picture. That's because a neural network can now repose human beings and change their clothes in photographs without losing key details. Badour AlBahar at Virginia Tech in Blacksburg and her colleagues at Adobe Research developed an algorithm that breaks down a source image into constituent body parts, with a neural network identifying where key joints and limbs are.

AI (Artificial Intelligence) seems to be the next big thing in many industries today. On Gartner's 2020 Hype Cycle of Emerging Technologies, for example, we find no less than seven explicitly AI-related trends in the first steep curve of inflated expectations--such as composite AI, generative AI, responsible AI, embedded AI, and explainable AI. For a term that dates back to 1956 and celebrates its 65th birthday this year, this seems remarkable, especially since the productive application of the currently hyped AI variations is expected to take another two to ten years. In this arena of promising AI technologies, the Dutch AI-based startup Lalaland is an interesting case. They have found a way to make AI work in a way that is both tangible and speaks to the imagination. Using AI technology, they are one of the front-runners that may change the online fashion industry and, arguably, make it more inclusive, sustainable, and profitable, thereby speaking to all three P's of the Triple Bottom Line.

Now that the majority of New York Fashion Week's runway shows have gone digital, designers are seeking to replicate the aura and grandeur of the fashion show outside of the catwalk's limitations. From Dior's live-streamed presentations, to Louis Vuitton's short films, to Loewe's FedEx-shipped "Show in a Box", high-fashion has demonstrated how collections can be shared with consumers in new, socially-distant ways. However, one of the main limitations of runway shows was the necessity of models -- and a lot of them. Real-time, in-person runways saw models walking out one after the other. With digital showings-- such as the pre-photographed Resort 2021 collections -- the necessity for more-than-a-couple-of-models is much lower.

Analyzing fashion trends is essential in the fashion industry. Current fashion forecasting firms, such as WGSN, utilize the visual information from around the world to analyze and predict fashion trends. However, analyzing fashion trends is time-consuming and extremely labor intensive, requiring individual employees' manual editing and classification. To improve the efficiency of data analysis of such image-based information and lower the cost of analyzing fashion images, this study proposes a data-driven quantitative abstracting approach using an artificial intelligence (A.I.) algorithm. Specifically, an A.I. model was trained on fashion images from a large-scale dataset under different scenarios, for example in online stores and street snapshots. This model was used to detect garments and classify clothing attributes such as textures, garment style, and details for runway photos and videos. It was found that the A.I. model can generate rich attribute descriptions of detected regions and accurately bind the garments in the images. Adoption of A.I. algorithm demonstrated promising results and the potential to classify garment types and details automatically, which can make the process of trend forecasting more cost-effective and faster.

Figuring out who and what is real or fake nowadays is getting to be a harder challenge in this AI-driven age. At CES, a buzzy startup with a Samsung pedigree, STAR Labs, introduced NEON as its first "artificial human." This "computationally created virtual being" sure looks and behaves like people you may come across every day, even if it doesn'tdo a whole lot right now, other than exhibit simple expressions and gestures on a large display. At this early preview stage, NEON is not quite a chatbot or robot and not quite a virtual assistant for your phone. But while NEON's can't be an exact copy or surrogate of an existing human, they are modeled after real people.

Information retrieval (IR) systems often leverage query data to suggest relevant items to users. This introduces the possibility of unfairness if the query (i.e., input) and the resulting recommendations unintentionally correlate with latent factors that are protected variables (e.g., race, gender, and age). For instance, a visual search system for fashion recommendations may pick up on features of the human models rather than fashion garments when generating recommendations. In this work, we introduce a statistical test for "distribution parity" in the top-K IR results, which assesses whether a given set of recommendations is fair with respect to a specific protected variable. We evaluate our test using both simulated and empirical results. First, using artificially biased recommendations, we demonstrate the trade-off between statistically detectable bias and the size of the search catalog. Second, we apply our test to a visual search system for fashion garments, specifically testing for recommendation bias based on the skin tone of fashion models. Our distribution parity test can help ensure that IR systems' results are fair and produce a good experience for all users.

Being a fashion model isn't as easy as it looks. Good looks go a long way, but presenting an outfit in the best possible light also requires a an exhaustive awareness of poses and the patience to perform for hours under hot lights in the studio or on the catwalk. AI has taken on a wide range of challenges in the last several years, and now machine learning researchers have set their sights on fashion models. A new research paper from Berlin-based unicorn fashion and technology company Zalando uses generative adversarial networks (GANs) to produce high-resolution images of virtual fashion models ready to model clothes of any style. The researchers set out to create an AI system capable of transferring customizable outfits and body poses from one fashion model to another.

Visualizing an outfit is an essential part of shopping for clothes. Due to the combinatorial aspect of combining fashion articles, the available images are limited to a pre-determined set of outfits. In this paper, we broaden these visualizations by generating high-resolution images of fashion models wearing a custom outfit under an input body pose. We show that our approach can not only transfer the style and the pose of one generated outfit to another, but also create realistic images of human bodies and garments.