Virtual Try-On Tools for Ecommerce

Virtual try-on tools are rapidly improving. The accuracy and experience of these tools are key to online fashion shopping as, even in their current state, they reportedly reduce returns and exchanges by 64%. The better the tools, the higher that percentage will get, the more comfortable people will be shopping online, and the more fashion brands will reduce costs and reduce the environmental impact of online returns. Street Fight has a great list breaking down their top 6 virtual try-on tools retailers are using: 🪞True Fit: An AI-driven tool that uses data input by shoppers to generate the right size in apparel and footwear. Probably the best-known on this list, True Fit is used by some of the largest retailers, including Madewell, Lululemon, and Gap. 🪞 WANNA | 3D & AR Experiences: AR technology where customers can visualize how apparel would look on their actual bodies. 🪞PICTOFiT: Avatar-based virtual try-on solution. Shoppers select their avatar, which represents themselves, and then dress the avatar in apparel. Customers can place their avatars in different environments, creating an engaging experience. PICTOFiT also integrates with Shopify, so purchases can be made directly from the try-on experience. 🪞3DLOOK: AI-driven mobile body scanning app that puts clothes directly on their ‘real’ bodies. 🪞Luna Solutions: AR tech exclusively for eyewear retailers. LUNA can show shadows and transparency of the glasses when worn and is used online and in-store by retailers, making the try-on process more efficient. 🪞Intelistyle A company that offers AI-styling solutions with virtual try-ons as one of their offerings, Intelistyle creates thousands of virtual models based on a brand’s specifications, allowing customers to see garments on models that most closely represent them. https://buff.ly/3FLhRFb

On this week's M7Unlock, ZERO10 is revolutionizing the retail industry by integrating Generative AI with Virtual Try-On (VTO) technology, setting new standards for realism and scalability in online fashion interactions. Their innovative approach towards interactive retail and VTO has been implemented by top brands like Tommy Hilfiger, Nike, Coach, and UGG. Project: Zero10's Augmented Reality Mirrors Industry: Fashion and Retail Key Differentiator: In-Store Try On Mirrors 🪞Virtual Try-On, Powered by Generative AI: ZERO10's virtual try-on technology powered by Generative AI allows for creating virtual fittings from single garment images, improving the realism and scalability of online retail. This evolving technology uses advanced AI techniques to capture detailed textures and fits, broadening the potential for e-commerce applications and enhancing user engagement with a more authentic try-on experience. 🧑🔬 Post-Pandemic Innovation: Consumer expectations have shifted, demanding that brick-and-mortar retail stores integrate e-commerce conveniences like easy access to products and discounts. An ever-decreasing percentage of consumers are satisfied with their shopping experiences, stressing the need for retailers to adopt digital solutions like AR to enhance both in-store and online customer engagement. 📊 Great Opportunity, Big Barriers: The development of accurate Virtual Try-On (VTO) technology presents significant technical challenges and data privacy concerns. Zero10 and its partners are navigating stringent data protection laws while developing AR solutions that offer realistic and privacy-conscious virtual fitting experiences, balancing innovation with ethical data practices. 🧠 For Brands: Retailers need to merge digital and physical shopping aspects to meet modern consumer expectations, as demonstrated by ZERO10's successful implementation of AR technology in stores. With many consumers expecting more, integrating AR could enhance customer interaction and ensure compliance with data privacy regulations, offering a competitive edge in the retail industry.

OOTDiffusion Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on Image-based virtual try-on (VTON), which aims to generate an outfitted image of a target human wearing an in-shop garment, is a challenging image-synthesis task calling for not only high fidelity of the outfitted human but also full preservation of garment details. To tackle this issue, we propose Outfitting over Try-on Diffusion (OOTDiffusion), leveraging the power of pretrained latent diffusion models and designing a novel network architecture for realistic and controllable virtual try-on. Without an explicit warping process, we propose an outfitting UNet to learn the garment detail features, and merge them with the target human body via our proposed outfitting fusion in the denoising process of diffusion models. In order to further enhance the controllability of our outfitting UNet, we introduce outfitting dropout to the training process, which enables us to adjust the strength of garment features through classifier-free guidance. Our comprehensive experiments on the VITON-HD and Dress Code datasets demonstrate that OOTDiffusion efficiently generates high-quality outfitted images for arbitrary human and garment images, which outperforms other VTON methods in both fidelity and controllability, indicating an impressive breakthrough in virtual try-on.

Maghan McDowell for Vogue Business exclusively about DRESSX GEAN AI https://lnkd.in/ev2qPrtG "... Using the DressX AI chatbot is surprisingly easy... ... This new capability, which was trained on the DressX fashion library, cuts the delivery time to about 24 seconds... ...Additionally, the new tool enables people to design their own pieces via text prompts... ... Among digital fashion and metaverse startups, DressX has some of the most significant traction and scale... ... It’s now exploring building out a business-to-business offering for DressX AI that enables retailers to add “instant dressing” to their own e-commerce sites for enabling virtual try-on. Going forward, it could also enable customers to dress themselves with prompts that are trained solely on their own intellectual property, so a brand could input its signature colours, silhouettes and prints, for example. This would work especially well with heritage brands who have large catalogues, Modenova says..." #fashiontech #fashionai #aifashion #dressx #digitalfashion