Digital dressing rooms: How generative AI is redefining virtual try-ons

Have you come across a retail marketing message lately that states, ‘Bring the fitting room home and find what you love’? Many retail brands today showcase their customer-first mindset through ‘try before you buy’ experiences, allowing customers to order products online, try everything, and return what they don’t like. While this increases customer satisfaction and trust, it also extends the length of the sales cycle and doesn’t immediately guarantee a purchase.

But what if your customers could bring the fitting room to their screens and finalize the purchase in just a few clicks? That’s where virtual try-ons and digital product visualization step in, changing how customers interact with products in the digital space. 

According to a recent survey, 44% of respondents have used virtual try-on features while shopping on retail sites, with 69% of them purchasing the product either online or in-store as a result.  

On the one hand, virtual try-on technology brings highly apprehensive shoppers on board with online shopping by allowing them to try on clothing, accessories, and more virtually in different environments—eliminating the hassle of returning products, thereby reducing return rates and enhancing the customer experience. On the other hand, digital product visualization enables retailers to create diverse visuals of their products using different backgrounds, objects, and models that deeply resonate with consumers’ imaginations. 

However, until now e-commerce product visualization has relied on costly, complex 3D rendering pipelines and real photo shoots that lack scalability and lead to challenges with expenses, time, logistics, and personalization. While virtual try-ons have heavily depended on augmented reality (AR), often falling short in realism, AI solutions can’t entirely replace photo shoots but can significantly scale and accelerate the process.

Thankfully, generative AI promises to improve these experiences—not by replacing existing technologies but rather by addressing their shortcomings, ushering in a new dawn for immersive and engaging product visualization.

How generative AI enhances virtual try-on and product visualization experiences

Generative AI enhances virtual try-on and product visualization by generating ultra-realistic images, leveraging prompts to guide models, and utilizing product photos instead of traditional 3D models:

Generate realistic images

For virtual try-ons, the technology provides users with a realistic visual experience that adapts to the surrounding environments and maintains accuracy, even under challenging conditions like poor lighting and low photo quality—realism that traditional 3D and Augmented Reality (AR) often struggle to achieve. In product visualization, generative AI rapidly generates photorealistic content, enabling retailers to visualize products in diverse environments and contexts, all facilitated by the use of prompts—another significant benefit of generative AI that we will discuss next.

Prompt-based generation

Generative AI offers a streamlined approach to product visualization that’s both simple and efficient. By using a text prompt to describe their desired outcome, users can take a photo of your product and generate a new background within seconds. Using prompts does not require technical skills or training, making it accessible for non-technical employees to easily generate diverse images. This allows for quick scaling of image creation across websites and marketing channels.

Use product photos over 3D models

The technology bypasses the need for 3D models of products, using product photos instead. Although it currently requires multiple images, advancements are quickly paving the way for it to operate effectively with just a single photo. This leap forward simplifies deployment for retailers and slashes costs, streamlining the path to more realistic and engaging virtual try-ons and product visualizations.

Now let’s delve into some examples of generative AI-powered virtual try-ons and product visualization use cases.

Virtual try-on

While virtual try-on is typically associated with clothing and fashion retail, this concept can also be extended to interior design and home decor. Generative AI facilitates both of these use cases. In some scenarios, the best approach is to combine generative AI with AR to achieve the optimal desired output. Here are some examples below.

Fashion

Clothing retailers can enhance online shopping experiences by using generative AI for realistic virtual try-ons. Unlike traditional methods that require fashion designers to create 3D models of garments, generative AI is trained directly on product images. The process involves generating an image of a customer wearing a specific item, using just the customer’s photo and the item they wish to try on. This technology produces highly realistic images that adapt to the lighting conditions and scene in the customer’s photo, offering a level of realism superior to that of existing 3D/AR solutions.

Interior design

Ever tried stretching your imagination to picture how your bedroom would look with medieval furniture or stylish new wallpaper? What about choosing new tiles for a bathroom? Navigating through dozens of design options can be daunting. Generative AI simplifies this process, allowing you to visualize and implement interior design changes effortlessly. 

Users can upload a photo of any room—furnished or not—and describe their vision through a prompt. Generative AI models respond by creating a photorealistic image that integrates real products, precisely matching the input description.

A standout application of generative AI in this domain is the visualization and selection of wall treatments, such as paints and wallpapers. While existing augmented reality methods for interior design rely on certain types of computer vision models for this task, generative AI improves both the process and the quality of the results. Users need to upload a photo of a room and select a paint or texture from the catalog. The AI then generates a photorealistic rendition of the room, showcasing the new wall treatment. Driven by user-friendly prompts, this approach simplifies decision-making and allows anyone to effortlessly envision and refine potential changes to their interiors.

Combining AR and generative AI to improve virtual try-on accuracy

Some retailers already using AR for virtual try-ons can improve the accuracy of results by integrating generative AI into their pipeline. Generative AI-powered virtual try-ons are evolving quickly. However, they currently require more computing power, which means they don’t work in real time with mobile cameras. Meanwhile, retailers with relatively simpler products, such as online jewelry stores selling rings, can still gain high-quality visualizations from existing AR setups. These AR visualizations guide the generative AI models for more precise image generation. By integrating generative AI with AR in post-processing, the resulting images are more natural and photo-realistic, with control over dimensions and other details. Check out the examples below.

Product visualization

While generative AI-powered virtual try-ons are truly game-changing for online retail, there are many other areas—including AI photo shoots, photorealistic virtual models, inclusive sizing, and ‘complete the look’—where generative AI excels in enabling product visualizations.

Visualizing products in new environments and contexts

Remarkably, 75% of online shoppers rely on product photos when deciding on a potential purchase, yet the high production costs of photoshoots and 3D rendering, along with the challenge of creating a large volume of diverse, high-quality images, often limit scalability and personalization. Generative AI addresses these issues by generating hyper-personalized, high-quality scenes featuring your products in different environments/contexts for web, mobile, marketing, and advertising.

The technology helps scale the production of these photos by allowing retailers to visualize products in fresh contexts. Imagine showcasing a winter jacket not just in a studio but on a snowy mountain peak or a rainy urban setting, all without leaving the office. Generative AI makes this possible by placing products in entirely new environments with controlled precision. Marketers and designers can adjust everything from composition, semantics, human poses, and more, significantly speeding up and scaling the creation of images across websites and marketing channels. This capacity to generate more content faster opens the door for diverse product setups, creating visuals that deeply resonate with consumers’ imaginations.

Color and material swap

While generative AI offers promising solutions, texture swapping remains a challenging task, particularly for complex materials. Currently, the technology doesn’t always produce perfect results, especially for intricate textures. However, it’s rapidly evolving and shows potential to gradually replace existing complex pipelines.

As the technology improves, it will increasingly enable retailers to render product variants without the need for extensive photoshoots or complex 3D modeling. For instance, a sofa might not need to be photographed in every available fabric. Instead, generative AI could create photorealistic renderings of each variant, streamlining the process of helping customers visualize choices and make decisions.

Photo-realistic virtual models

Generative AI-powered photorealistic virtual models can help reduce costs associated with expensive photo shoots, especially in clothing retail. Integrating Generative AI with mannequins allows retailers to actively dress them in apparel, capture photos, and then replace them with photorealistic virtual models. Similarly, retailers can leverage 3D product assets by placing them on virtual mannequins and then swapping them with photorealistic generative AI virtual models. This innovative approach ensures that retailers can cost-effectively showcase their products with stunning realism and flexibility.

Bridging the gap in inclusive sizing

While AI photo shoots and photorealistic virtual models improve customer engagement, how do you make sure customers fall in love with a product at first sight and know it is right for them immediately? Imagine it was that mini red dress they always wanted, but this time, they found a model wearing it who had the same body type as them. So, they decide to buy it even without a try-on—this could shorten the sales cycle even further. However, many brands are yet to expand their product visualizations beyond traditional sizes.

Even though inclusive sizing is a major trend shaking up the retail world and many retailers are eager to embrace this shift, budget and time constraints often stand in the way—hiring diverse models and conducting photo shoots for every body type becomes too costly and time-consuming. This is precisely the problem we can solve using generative AI. Major players like Google and Walmart already have this feature. Using AI in this context isn’t just about cutting corners; it’s about opening doors to create a more inclusive, personal, and sustainable shopping experience for fashion brands, retailers, and customers. It efficiently generates visualizations using photorealistic virtual models across a spectrum of sizes, making inclusive retail accessible for customers of all shapes. Retailers can now showcase a broader range of sizes, ensuring everyone feels seen and catered to. To maintain trust, retailers must be transparent with customers, noting that some images are AI-generated previews, offering a glimpse of how products might look in various sizes. This approach keeps fashion forward and inclusive, without breaking the bank.

Enriching ‘complete the look’ visualization

‘Complete the Look’ is a modern online retail feature that elevates the shopping experience by suggesting complementary items to match a product a customer is viewing. Traditionally, these recommendations are displayed as separate items against a plain background, which may not fully convey how the items look when styled together. 

By integrating generative AI, retailers can transform this feature into a dynamic and interactive shopping experience. Generative AI can create realistic images of virtual models combining different recommended products. For example, when a customer views a summer dress, generative AI can produce an image of a model wearing the dress paired with suggested accessories like sunglasses, sandals, and a handbag. This capability can also extend to virtual try-ons using the customer’s image. A visually rich presentation allows customers to envision the complete outfit in a real-life context, positively impacting decision-making.

Similarly, in home decor, when a customer looks at a sofa, generative AI can generate images of fully decorated living rooms, showcasing the sofa with recommended throw pillows, a matching rug, and wall art. This inspires the customer and demonstrates how products complement each other in a practical setup.

These enhanced product visualizations encourage customers to add more items to their cart, driven by a clearer vision of the product’s utility and appeal in a cohesive look, potentially leading to a higher average order value (AOV) and increased customer satisfaction.

Navigating limitations as technology evolves

As generative AI promises to reshape virtual try-ons and product visualizations in retail, we must balance its exciting potential against the real challenges it faces, especially as the technology continues to evolve rapidly.

• A poorly executed virtual try-on could deter customers rather than facilitate a purchase. If using the tool becomes a hassle, involving steps like uploading photos or entering detailed measurements, customers might just skip it.
• The need for personal photos raises significant privacy issues. Customers are understandably cautious about how their data is used and shared, especially when sensitive images are involved.
• There’s a narrative that generative AI might be taking jobs from size-diverse models. While it’s true that generative AI can help bridge gaps caused by budget and time constraints, it’s crucial to balance technology adoption with human employment, which includes elements of social proof and authenticity. Gen Z shoppers, in particular, have a keen sense for authenticity.
• Generative AI-powered virtual try-ons are currently too slow for real-time use compared to AR, but the technology is rapidly improving and we expect these models to run efficiently even on mobile devices in the near future.
• The problem of fidelity remains a challenge. Generative AI generates ultra-realistic product visualizations, but achieving high fidelity for complex products such as clothing items with complex patterns/prints or small details, can be difficult. This issue varies by type of product and brand. Generally, fast-fashion companies may prioritize speed and cost over perfect fidelity, while more unique or luxury brands require flawless quality to maintain their brand standards and ensure customer satisfaction.
• Generative AI offers improved visualization in various contexts and superior quality compared to AR or 3D models. It helps users better understand how a product may look, even if the sizing isn’t perfect. Sizing accuracy is addressed by other types of ML models, and there is room for future improvements by combining both technologies.

Despite these limitations, there are compelling reasons to use the technology. Research shows that 55% of online apparel shoppers have returned an item because it looked different on them than expected, so having a virtual try-on experience could be more beneficial than not having one. Additionally, 42% say they don’t feel represented by images of models while shopping online for clothes. While retailers explore budget and time issues to employ size-inclusive models, generative AI-powered inclusive sizing can help cover the existing gap.

For retailers, this could mean fewer returns, higher engagement, an increase in Gen Z and millennial customers, and, most importantly, more sales—and it’s proving to be successful: According to Snap, Dior saw a 6.2x return on ad spend when implementing its try-on technology. 

With rapid innovation, we will see many of the limitations diminish while new ones may emerge. AI-generated images may not currently be suitable for every brand, and this will change over time.

Final thoughts: Adopt solutions and starter kits to enable modern visual experiences

Every year, e-commerce experiences reach new heights. Just as customers were getting used to social commerce, digital wallets, and personalized content, generative AI swooped in to introduce more interactive experiences than ever before. The technology performs best when models are trained on high-quality, large datasets of product images, backgrounds, and models. As leading data and AI implementation partners like Grid Dynamics rapidly advance this technology, we expect to see more optimized and cost-effective solutions, making it accessible to every retailer.

To kickstart your journey with generative AI-powered product visualization—enabling you to enjoy the benefits of AI photo shoots, virtual try-ons, virtual models, and support for body positivity—our experts have developed a range of solutions and starter kits. Contact us for a discovery session today.

Generative AI for product images

Transform your product design process with Grid Dynamics’ Generative AI for product images solution! Empower your teams to create stunning designs, hyper-localized visual content, and personalized customer experiences in seconds. Boost conversion rates with immersive web rooming and revolutionize virtual try-ons in apparel, jewelry, and cosmetics retail. 

Product visualization with Generative AI

Discover how we’re leveraging state-of-the-art models like DALL-E and Stable Diffusion to automate and streamline visual content creation processes, slashing costs and boosting efficiency. Explore real-world examples and see firsthand how Generative AI is reshaping the online shopping experience for brands and consumers alike. But that’s not all! Learn how we’re overcoming the challenges of controllable image generation and fine-tuning models to unleash their full potential, pushing the boundaries of what’s possible in visual content creation.

Generative AI Product Design Starter Kit

Transform your design process with our Generative AI Product Design Starter Kit. Instantly generate design variations based on sketches, references, and text. Customize shapes and styles effortlessly. Empower customers with intuitive editing tools. Alter textures, styles, and lighting seamlessly. Render realistic previews from textual descriptions. Captivate your audience with immersive visualizations. Experience rapid prototyping, effortless customization, and seamless rendering today.

Generative AI offerings

Unlock the exemplary power of generative AI for your business with our suite of Generative AI offerings. Whether you’re in retail, healthcare, manufacturing, or financial services, there’s a generative AI solution tailored to meet your specific needs. Enhance customer experiences, drive innovation, and streamline operations with personalized content creation, virtual simulations, and virtual assistants. Discover the limitless possibilities of generative AI and propel your organization toward unprecedented success.

Augmented reality in space organization

While generative AI is making significant strides in virtual try-ons, We acknowledge that this technology is rapidly evolving, offering endless possibilities for exploration. However, it’s equally important to appreciate the established role that AR plays in enhancing visual experiences. With its proven track record, AR continues to offer valuable solutions, complementing the advancements brought forth by generative AI. Grid Dynamics revolutionized the customer buying experience for a leading specialty retailer in the storage and space organization market using Augmented Reality. Discover how we transformed tedious measurement processes into seamless, AR-powered solutions, enhancing convenience and boosting engagement.