What Was Wrong With Virtual Try-On Before 2025 — And Why It Finally Works Now

Fashion e-commerce has struggled for years with unclear product visuals and high return rates. Shopify reports that inaccurate or misleading photos are a major source of hesitation during online shopping.¹

Statista shows that return rates in online fashion stay between 24% and 40%, largely because the product doesn't look or fit as expected.² Virtual try-on had the potential to fix this — but for years, the technology simply wasn't ready.

Early VTO Tools Were Built on Models Not Designed for Fashion

Between 2020 and 2024, most virtual try-on solutions relied on open-source generative models made for general imagery. They didn't understand how clothing behaves, how fabric drapes, or how silhouettes should respond to different body types. The result was often distorted or flat. Textures blurred, volumes collapsed, and lighting didn't match the original photo. Instead of helping customers understand the product better, early VTO often made the garment look less appealing.

Dozens of Similar Tools All Faced the Same Problems

As generative AI became widely accessible, many startups entered the VTO space. But because most used the same base technology, the limitations were visible everywhere. Results changed unpredictably based on pose, lighting, or body type, and retailers struggled to maintain consistency.

Baymard Institute notes that unclear or untrustworthy visuals significantly increase hesitation and return risk.³ Instead of reducing uncertainty, early VTO often amplified it.

Real Garment Physics Were Missing

Another major limitation was the absence of real fabric behavior. Early VTO didn't understand the weight of denim, the fluidity of satin, or the structure of leather. The garment didn't adapt to the customer's body — it simply appeared on top of the photo without true interaction. Shoppers couldn't see how a piece would fall on their shape or whether the cut would flatter them. Without fabric and shape accuracy, VTO couldn't support good purchasing decisions.

Body Understanding Was Too Weak to Be Reliable

Early systems also struggled to interpret the human body in everyday conditions. Bent arms, natural poses, hair overlap, plus-size silhouettes, and non-studio lighting often caused the garment to warp or the body shape to shift unnaturally. Fashion requires precision. Early VTO simply didn't have the technical ability to provide it.

In 2025, the Technology Finally Shifted

In 2025, virtual try-on underwent a substantial leap. Purpose-built model architectures, garment-aware segmentation, physics-informed rendering, and improved lighting consistency brought a level of realism the industry had never seen. For the first time, VTO quality was high enough for PDPs, lookbooks, marketing automation, and even full-outfit styling. Fabrics behaved correctly, body proportions stayed consistent, and the outputs looked like real on-body photos. Virtual try-on finally became something customers could trust — not a novelty or a demo.

Summary Insight

Early VTO failed because it couldn’t interpret garments, fabrics, or body shapes; 2025’s purpose-built models finally deliver reliable, trustable virtual try-on.

How Hautech Supports This Shift

Hautech delivers photorealistic virtual try-on designed specifically for fashion. It works with any customer selfie, preserves fabric behavior, keeps body proportions consistent, and instantly adapts to existing catalog photos. This helps brands reduce return rates, increase confidence, and elevate the shopping experience across the full customer journey.

Footnotes and References