Virtual Fitting Room Technologies To Boost Retail Sales in a Post COVID-19 World

Virtual Fitting Room Technologies To Boost Retail Sales in a Post COVID-19 World

Virtual Fitting Room Technologies To Boost Retail Sales in a Post COVID-19 World. The goal of this article is to describe how these systems work from the inside.

I hate shopping in brick and mortar stores. However, my interest in virtual shopping is not limited to the buyer experience only. With the MobiDev Data Science department, I’ve gained experience in working on AI technologies for virtual fitting room development. The goal of this article is to describe how these systems work from the inside.

How Virtual Fitting Technology Works

A few years ago, the "Try before you buy" strategy was an efficient customer engagement method in outfit stores. Now, this strategy exists in the form of virtual fitting rooms. Fortune Business Insights predicted that the virtual fitting room market size is expected to reach USD 10.00 billion by 2027.

To better understand the logic of virtual fitting room technology, let's review the following example. Some time ago, we had a project of Augmented Reality (AR) footwear fitting room development. The fitting room works in the following way:

  1. The input video is split into frames and processed with a deep learning model which estimates the position of a set of specific leg and feet keypoints.
  2. A 3D model of footwear is placed according to the detected keypoints to display the orientation to a user naturally.
  3. A 3D footwear model is rendered so that each frame displays realistic textures and lighting.

When working with ARKit (Augmented Reality framework for Apple’s devices) we discovered that it has rendering limitations. As you can see from the video above, the tracking accuracy is too low to use it for footwear positioning. The cause of this limitation may be the maintenance of the inference speed while neglecting the tracking accuracy, which might be critical for apps working in real-time.

One more issue was the poor identification of body parts by the ARKit algorithm. Since this algorithm is aimed to identify the whole body, it doesn’t detect any keypoints if the processed image contains only a part of the body. It is exactly the case of a footwear fitting room when the algorithm is supposed to process only a person's legs. 

The conclusion was that virtual fitting room apps might require additional functionality along with the standard AR libraries. Thus, it’s recommended to involve data scientists in developing a custom pose estimation model supposed to detect keypoints on only one or two feet in the frame and operate in real-time.


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