Rocket Science Behind Virtual Try‑On
In the competitive e-commerce landscape, brands constantly seek technologies to transform the online shopping experience.
Virtual try-on (VTO) technology is a prime example of such innovation, leveraging advanced computational tools and rendering capabilities to simulate a highly realistic and personal interaction with products online.
This capability is not just a luxury—it’s becoming a necessity for modern consumers who demand interactive online shopping experiences.
Understanding the complex ’rocket science’ behind VTO is crucial for leveraging its full potential, driving sales, and enhancing customer satisfaction.
Understanding Virtual Try‑On Technology
The evolution from 2D to 3D in e-commerce marks a journey that has changed how consumers engage with products online. In the early stages of e-commerce, product images were two-dimensional, offering static representations of items. As technology advanced, a paradigm shift emerged towards three-dimensional visuals. This evolution facilitated a more immersive online shopping experience.
Integrating 3D product images allows customers to view items from multiple angles, providing a comprehensive understanding of their features. VTO experiences took the 3D evolution further. This progression from static 2D images to dynamic 3D visuals and immersive VTO experiences has offered consumers a more tangible way to explore products before purchasing.
VTO operates on a foundation of advanced technologies, leveraging augmented reality (AR) and computer vision to create realistic experiences. At its core, the process involves several intricate steps. First, computer vision algorithms find human (or human body parts) on the image. This data is then used to map the virtual representation of the selected product onto the user’s body or face with high precision.
A key element is the real-time tracking of the user’s movements. Through continuous analysis of the user’s gestures and positioning, the technology adjusts the virtual overlay accordingly, ensuring a dynamic try-on experience. This is made possible by integrating machine learning algorithms, continuously learning, and refining their understanding of human anatomy and behavior over time.
Rendering Engine Details
AR try-on technology relies heavily on sophisticated rendering engines that are both compact and efficient, allowing for high-quality real-time graphics on various devices. These engines, typically around 2 MB in size, are designed to operate within the constraints of mobile devices and web browsers, delivering detailed visuals without compromising performance. This balance is vital for maintaining fluid user interactions during the virtual try-on, ensuring that the digital overlays of clothing or accessories align seamlessly with user movements and real-world conditions.
Rendering drives the visual fidelity of VTO experiences, and WANNA stands out by harnessing the capabilities of mobile phone processors to achieve high-quality rendering. WANNA’s approach enables realistic visuals, ensuring users enjoy a compelling experience. This achievement is attributed to the efficient utilisation of graphic processing, where WANNA’s rendering engine performs swiftly to deliver top-notch performance.
WANNA’s proprietary rendering engine, designed to work seamlessly across all platforms, gives the company full control over critical elements necessary for a smooth VTO experience. It is compact (around 2 MB) compared to gaming engines like Unity (around 20 MB), and it provides the same image quality in mobile apps and web browsers. It also supports a variety of complex materials, including gemstones. This level of control allows WANNA to optimize aspects such as texture mapping, lighting, and shading, ensuring a consistent and high-quality visual representation of virtual products.
Challenges in 3D Modeling
Creating realistic 3D models for VTO presents significant challenges, particularly in achieving high fidelity within tight budgets. The process requires detailed texturing and careful optimisation to ensure models look realistic and respond dynamically to user interactions. This is crucial for luxury brands where detail and texture are paramount. The challenge is heightened by the need to keep these models lightweight enough to function smoothly on standard consumer-grade hardware.
Three limitations hinder the widespread adoption of real-time VTO: achieving photorealistic 3D quality standards, scaling the technology efficiently, and managing costs. The computational constraints of web browsers pose another limitation, influencing the extent of what can be achieved. Current limitations in mobile phones and AR headsets compound these challenges, necessitating meticulous 3D asset optimization for seamless AR and web-based 3D experiences. Load times are also critical, impacting revenue loss on e-commerce platforms. WANNA is actively addressing these challenges, finding the delicate balance between high-quality 3D models and optimization for lightweight, quick immersive experiences, and making significant strides in revolutionizing VTO and 3D interactions on the web. WANNA’s proprietary rendering engine and strategic focus on seamless web integration showcase the commitment to overcoming the limitations of mobile devices and web browsers.
Datasets: Collection, Diversity, and Challenges
Datasets are fundamental to the effectiveness of virtual try-on. Data collection involves acquiring or creating extensive datasets encompassing diverse body types, clothing styles, and textures. This dataset becomes the training ground for machine learning algorithms, allowing them to learn and understand the intricate relationships between virtual garments and the human body. These algorithms employ computer vision techniques to analyze and interpret the data, extracting essential features such as body contours, size, and movement patterns. The algorithms then apply this knowledge in real-time simulations, enabling virtual garments to adapt dynamically to user interactions. Iterative feedback loops between data collection and algorithm refinement are crucial for continuously enhancing the realism of the VTO experience. The synergy between comprehensive datasets and sophisticated algorithms ensures that virtual garments drape realistically and provide users with an immersive and accurate representation of how clothing, footwear, or accessories would look in the physical world.
A proprietary dataset allows for a tailored and specific representation of the products or items within the virtual environment. WANNA puts a lot of effort into building datasets for each experience, making it optimally representative for each category. We go beyond standard videos and include as many use cases as possible, paving the way for a mass VTO adoption. Owning a dataset facilitates control over the quality and diversity of the data, enabling the training of ML models on a more representative sample. This, in turn, enhances the accuracy of the virtual try-on application, ensuring it performs optimally across a range of body types, styles, and textures.
Creating a proprietary dataset for VTO experiences is a meticulous and time-intensive process, reflecting the intricate nature of training models to simulate real-world scenarios accurately. From inception to completion, the timeline is demanding and takes six months minimum.
Fast Deployment: «60 Minutes to Tech on Any Website»
WANNA is an industry leader in VTO and 3D experiences, exemplified by its highly realistic VTO for the web. We have strategically focused on crafting a VTO solution that excels in seamless web integration. This approach is precious for luxury brands without dedicated mobile applications, ensuring a broad reach. What sets WANNA apart is its deployment efficiency, taking just 60 minutes to integrate its VTO technology onto any website, eliminating the need for additional apps. Moreover, WANNA’s VTO experiences are designed with versatility, as links to these experiences can be effortlessly shared across popular platforms such as Instagram, WeChat, and TikTok. This level of accessibility and realism positions WANNA as an excellent solution for businesses seeking to elevate their online presence and offer customers an immersive VTO on the web.
Neural Networks: Power, Compute Challenges, and Optimization
Neural networks play a crucial role in the VTO experience, particularly in real-time tracking and adjustment of virtual items on the user.
The biggest limitation of virtual try-on technology is launching a neural network on a mobile phone. Neural networks are quite heavy and demand large computational power, making it challenging to launch neural networks on mobile devices in real-time. Complex optimisation is needed to get a good result with the moderate computational power of a mobile phone, especially if it’s a neural network with high-quality tracking.
Another component is mobile render (fast and small, optimised for mobile devices and e-commerce). Creating a high-performance render that delivers good FPS on mobile devices is challenging. WANNA’s render has a small footprint and can be deployed in client apps. It embraces three key parameters for a great VTO experience — quality, size, and price- and provides high quality with limited resources.
As the demand for mobile-centric experiences continues to rise, VTO technology must navigate these compromises to provide users with engaging virtual try-on applications on the devices people use daily.
Conclusion
VTO, with its ability to engage users and cultivate an emotional connection with products, necessitates intricate technical solutions to simulate realistic interactions between virtual items and users in real time. Achieving seamlessness in VTO demands substantial investments in augmented reality, machine learning, and advanced rendering engines. The significance of understanding the «rocket science» behind virtual try-on technology lies in appreciating the complexity of the underlying algorithms, data processing, and real-time simulations required to create an immersive virtual experience. This technical prowess is essential for enhancing user satisfaction and empowering brands and retailers to deliver a competitive shopping experience in the dynamic e-commerce landscape.
