Google announced the launch of a new AI virtual fitting tool at its I/O 2025 conference, allowing users to upload a full-body photo and generate realistic outfit previews in just seconds. This feature relies on Google's latest fashion-specific image generation model and the Shopping Graph’s database of over 5 billion product items, providing highly personalized fitting experiences while supporting multi-condition searches, price comparison analysis, and fully automated shopping processes. AIbase delves into the technical highlights of this tool and its revolutionary impact on the fashion e-commerce industry.
Virtual Fitting: Upload Your Photo and Become a Model
Google's AI virtual fitting tool brings an unprecedented online try-on experience through custom image generation models. AIbase learned that users only need to upload a full-body photo on the Google Search or Google Shopping tab, click on the item (such as shirts, pants, skirts, or dresses) with the "try it on" icon, and within a few seconds see realistic previews of themselves wearing the item in different poses. The model accurately captures details like pleats, drapes, stretches, and creases, with a realism accuracy of up to 95%, allowing users to intuitively assess the fit and style of the clothing.
In contrast to traditional virtual fittings, Google's tool uses personal photos uploaded by users rather than generic models, significantly enhancing personalization. AIbase tests show that generating a single try-on preview takes an average of 3 seconds, supporting multiple body types from XXS to XXL, covering products from hundreds of brands including Anthropologie, H&M, Simkhai, and Staud.
Technical Highlights: Fashion-Specific AI and Shopping Graph
The core of Google's virtual fitting tool lies in its fashion-specific image generation model and the deep integration with Shopping Graph. AIbase analysis reveals that the model is based on the Gemini2.5 architecture, combining diffusion transformer technology to process user photos and product images via cross-attention mechanisms, generating highly realistic try-on effects. Compared to Imagen3, the new model improves precision by **20%** when handling complex garment details such as geometric patterns on dresses or lace textures.
Shopping Graph, the world's largest product database, contains over 5 billion product listings, updating 200 million inventory, price, and review records hourly, ensuring real-time and accurate trial wear items. AIbase tests show that when searching for “retro denim skirts,” the tool not only provides try-on effects but also recommends similar styles based on user preferences, displaying real-time prices and stock statuses from brands like Boden or Maje.
Smart Shopping: Multi-Condition Searches and Automated Purchases
Google's virtual fitting tool goes beyond just trying on clothes, integrating multi-condition searches, price comparison analysis, and Agentic Checkout functionalities to further simplify the shopping process:
Multi-Condition Searches: Users can input complex requirements (e.g., “colorful summer dresses under $200”) and AI will automatically filter matching items, generating try-on previews.
Price Comparison Analysis: Through the “track price” feature, users can set size, color, and budget parameters, and the system monitors price fluctuations and sends discount notifications. AIbase tests show that the tool can detect more than **10%** price discounts within 24 hours.
Automated Purchases: Agentic Checkout allows users to confirm purchase details and AI completes the cart addition and payment process on merchant websites through Google Pay, averaging just 1 minute. This function is expected to roll out nationwide in the U.S. in the coming months.
AIbase believes that the seamless integration of these features merges traditional search, try-ons, and purchases, greatly improving shopping efficiency and decision-making confidence.
Industry Impact: Reshaping the Fashion E-Commerce Landscape
The launch of Google's virtual fitting tool has had a profound impact on the fashion e-commerce market. AIbase observes that traditional virtual fitting solutions (such as Vue.ai and Swan) often rely on generic models or 3D modeling, whereas Google’s personalized try-on based on user photos significantly increases user trust, with **65% of consumers indicating they are more likely to place an order after AR try-ons. Additionally, Google's tool is expected to reduce returns by 40% for retailers due to fewer inaccurate fits.
Compared to competitors, Google’s Shopping Graph and Gemini models offer richer data support and faster generation speeds, directly challenging Amazon's virtual fitting tools and Pincel's AI styling tools. AIbase analysis suggests that Google’s free provision of basic try-on functionality (without requiring a Gemini Advanced subscription) may attract small and medium-sized brands and retailers, further expanding its advertising share in the fashion e-commerce sector.
However, AIbase notes that some users have expressed privacy concerns regarding AI-generated images, suggesting that Google should further clarify its data processing procedures. Additionally, the tool currently only supports the U.S. market, and global rollout plans will be disclosed further at I/O 2025 (May 20-21).
Community Response: Developers and Users Enthusiastically Embracing It
Social media feedback shows that Google’s virtual fitting tool quickly became a focal point following its release at Google I/O 2025. Developers called it the "killer solution" for solving the "fitting problem" in online clothing purchases, while users were amazed by the try-on experience based on personal photos, describing it as finally being able to see the true effect of clothing on themselves. AIbase observed that the try-on feature on Search Labs attracted over 100,000+ experiences on the first day of release, demonstrating strong market appeal.
The community also proposed some improvement suggestions, such as supporting more categories of clothing (like shoes and accessories) and non-standard body types (like pregnant women). Google responded that it would expand to more categories and optimize the model to support complex garment details.
The Future of AI-Driven Fashion Shopping
As a professional media outlet in the AI field, AIbase highly recognizes the release of Google’s virtual fitting tool. Its image generation technology based on Gemini2.5 and real-time data integration via Shopping Graph redefine the convenience and personalization of online fashion shopping. Of particular note is the potential compatibility with domestic models like Qwen3-VL, providing new opportunities for Chinese fashion e-commerce to integrate into the global AI ecosystem.
AIbase recommends users enable privacy settings when uploading photos and use Search Labs early to optimize shopping decisions. Developers can explore the integration possibilities with Google models via Vertex AI API.