Virtual Try-On: Building Augmented Reality Solutions

min. read

February 12, 2025

Virtual Try-On: Building Augmented Reality Solutions

Virtual try-ons are transforming online shopping by using augmented reality (AR) to let customers visualize products in real-time. This reduces returns by 25% and increases willingness to pay by 40%. Here's how it works:

AR Tools: Platforms like ARKit and ARCore enable spatial tracking and object recognition.
3D Models: High-quality, optimized 3D assets ensure realistic visuals.
Face & Body Detection: Advanced tracking maps facial points and body joints for accurate placement.

Key Benefits:

Fewer returns and better customer engagement.
Cross-platform compatibility for wider accessibility.
Integration with business systems for inventory and personalized recommendations.

Want to build your own AR try-on system? Focus on combining precise tracking, optimized 3D assets, and seamless integration with your business tools.

How To Build an AR Fashion Lens for Virtual Try-On

Main Elements of AR Try-On Systems

Creating effective virtual try-on solutions involves three key technical components that must work together smoothly. Each part is essential for delivering precise and responsive AR experiences.

AR Development Tools

The backbone of any virtual try-on system is the AR development platform. Popular options include Apple's ARKit for iOS and Google's ARCore for Android, which provide essential features like spatial tracking and object recognition. Each has its strengths and limitations, so choosing the right one is critical for accurate product visualization.

Option	Pros	Cons
Ready-made Solutions (e.g., FittingBox)	Quick setup, proven performance	Limited customization, higher licensing fees
Custom Development (Unity/Unreal)	Flexible features, advanced options	Longer development time, requires expertise
Web-based AR	Easy browser access, no app needed	May perform poorly on older devices

3D Product Models

Creating lifelike 3D models is a meticulous process that requires attention to detail. For example, Prada's virtual try-on system showcases the importance of precision, using sunglasses models with millimeter-level accuracy^[1]^[3].

High-quality models typically involve:

At least 10,000 polygons for realistic detail
PBR materials for accurate textures
UV unwrapping for proper mapping
LOD (Level of Detail) optimization for smooth performance

These models integrate seamlessly with tracking systems to ensure accurate placement and visualization in real time.

Face and Body Detection

State-of-the-art virtual try-on systems rely on advanced computer vision for precise tracking. Sephora's Virtual Artist is a standout example, using facial recognition technology that maps over 150 facial points to create realistic makeup simulations^[1]^[3].

Key technologies include:

Facial landmark detection with 68-point modeling
Depth sensing via LiDAR or ToF cameras
Full-body tracking using pose estimation networks

To maintain smooth performance, these systems balance accuracy with speed through multi-threaded rendering and optimized machine learning models. This optimization is crucial for delivering a seamless experience and supporting the reduced returns and higher conversions linked to AR try-on tools.

Building AR Try-On: Step by Step

Project Setup

A European fashion retailer successfully rolled out an AR try-on feature, attracting over 30,000 users who tested multiple sizes during its launch ^[1]^[2]. Their success stemmed from clear goals and measurable outcomes.

Key setup components included:

Market Analysis: Mapped customer journeys and tracked baseline metrics, such as a 17.6% apparel return rate.
Technical Planning: Dedicated 40% of development time to 3D modeling and set device performance benchmarks.
Success Metrics: Focused on tracking user engagement and reducing return rates.

Once objectives were set, the team prioritized a balance between technical accuracy and user experience during development.

Initial Development

The development phase focused on ensuring both tracking accuracy and performance. Modern AR try-on solutions rely on several technologies:

Real-time Processing: Optimized pipelines to deliver instant responses.
Rendering Pipeline: IKEA Place uses a smart rendering system, showing low-detail models for previews and switching to high-resolution models after selection ^[2].
Quality Assurance: Extensive testing across different devices and environments to ensure reliability.

Cross-Device Support

After establishing the core functionality, developers tackled platform-specific challenges using flexible solutions. ASOS, for example, adjusted virtual fitting room geometry dynamically to manage resources effectively across devices ^[3].

Ray-Ban’s approach highlights the benefits of a modular system:

Tracking: Used platform-specific SDKs to optimize performance for each device.
Rendering: Leveraged WebGL, Unity, and Metal to maintain visual consistency across platforms.
Processing: Integrated cloud APIs for scalable backend operations.

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Technical Setup and Speed

Required Components

Once cross-device functionality is in place, developers need to focus on three essential technical elements to ensure smooth real-time performance. For example, Warby Parker achieves response times of 200ms by leveraging edge computing for pose estimation ^[1]^[2]. This involves:

Real-time facial landmark detection using 68-point models
Optimized rendering pipelines to maintain consistent frame rates

Nike Fit's now-retired AR shoe sizing system highlighted the importance of matching hardware requirements to the target audience's devices. This alignment is crucial for achieving the performance goals discussed earlier in the article ^[1]. Modern solutions should meet these device specifications:

Component	Minimum	Optimal
Camera	720p	1080p+
GPU	OpenGL ES 3.0	Vulkan/Metal

Speed Improvements

MobiDev ensures 60 FPS performance for apparel try-ons by using Vulkan API optimizations and asynchronous loading techniques ^[2]. Their strategy includes:

Model Optimization: Reducing polygons by 40-60% while incorporating HDR environment mapping
Lighting Management: Using HDR environment mapping to enhance material realism

Sephora Virtual Artist employs real-time ambient light estimation, HDR environment mapping, and adaptive texture streaming to achieve high-quality visuals ^[2]^[7]. Their method involves:

HDR environment mapping for dynamic relighting
Automatic reflectivity adjustments based on detected luminance

Zalando monitors specific metrics for its virtual fitting room, reporting an average of 2.4 try-on sessions per visit ^[1]^[2]. These technical improvements directly contribute to reducing returns and increasing conversions, as discussed earlier.

Connecting with Business Systems

After ensuring strong technical performance, AR solutions need to work seamlessly with business systems to deliver measurable results.

Customer Data Integration

Linking AR try-ons with customer databases allows for more tailored experiences. This connection supports reduced returns and higher conversions by bridging AR interactions with business operations. A great example is Sephora's Virtual Artist, which integrates with Braze to send automatic abandoned cart reminders when users try but don’t purchase specific lipstick shades ^[1]^[10].

Here are three main components of this integration process:

Component	Purpose	Tools
Authentication	Secure data access	ARShades' OAuth 2.0 for Salesforce/HubSpot ^[4]
Data Sync	Real-time profile updates	Banuba's REST APIs for purchase history ^[9]
Privacy Controls	Compliance management	Zalando's pseudonymized measurements ^[1]^[5]

To make personalization effective, focusing on specific customer data is key. For instance, Warby Parker’s frame recommendation system uses past purchase data and facial measurements. Similarly, Sephora employs a 16-point complexion mapping system to match products accurately ^[1].

Stock and Results Tracking

Integrating AR with business systems also enhances inventory and performance tracking.

Tangiblee’s platform is a standout example, syncing directly with Shopify to provide live inventory updates, which helps avoid overselling ^[11]. Their system delivers notable results:

"Tangiblee users achieve 19% higher AOV through stacking features, while their clients report significant improvements in inventory management efficiency" ^[11].

Analytics integrations also show how AR impacts operations. For instance, Océane’s nail polish try-on reduced overproduction by 40% by analyzing regional trends ^[9]^[10].

To maintain accurate inventory tracking, connect AR systems to ERP platforms via SKU-level APIs. Use WebSockets to ensure instant updates across all sales channels.

Summary

Successful AR try-on solutions bring together key technical elements like computer vision, 3D modeling, and cross-platform compatibility, while seamlessly aligning with business strategies. These components directly contribute to the business outcomes highlighted in earlier case studies.

Here are the three main technical pillars of effective AR implementations:

Computer vision: Features such as 68-point facial tracking for accuracy.
3D modeling: Use of multi-resolution assets paired with PBR (Physically Based Rendering) materials.
Cross-platform compatibility: Device-optimized rendering pipelines for smooth performance.

Ensuring strong performance is critical for broader adoption. The 42% year-over-year growth in WebAR solutions ^[6] highlights the importance of making AR accessible and easy to use.

Technical teams should focus on testing solutions in diverse real-world conditions and use automated quality controls to refine 3D asset optimization. For example, Banuba’s progressive loading system effectively balances resource management with maintaining high visual quality ^[6].

Looking ahead, advancements like generative AI will enable dynamic product variations, building on optimized 3D models and real-time tracking capabilities ^[12]. When paired with the technical components and business strategies discussed, these innovations promise to further streamline e-commerce operations.

FAQs

How to implement virtual try-on?

To set up a virtual try-on, you'll need to integrate AR frameworks, 3D assets, and tracking systems. Choose tools that align with your business goals and meet the technical needs outlined in the AR Development Tools section.

How do virtual try-ons work?

Virtual try-ons use a mix of technologies to create realistic product visualizations. These systems rely on face and body detection technologies, as explained in the Main Elements section, and can track over 33 body joints for clothing visualization ^[2]^[3].

For accuracy, eyewear requires approximately ±2mm precision (using face mesh tracking), while clothing needs about ±5cm accuracy (via body segmentation).

How to develop a virtual try-on?

Developing a virtual try-on involves a three-phase process described in Building AR Try-On: Step by Step. This approach incorporates performance improvements discussed in the Technical Setup and Speed section.

Important factors include using multi-resolution 3D models, ensuring device-agnostic rendering, and managing adaptive lighting ^[8].

"Testing across device capabilities remains essential for adoption" - MobiDev AR Team Lead ^[2].