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Be Your Own Model

AR-guided photo capture flow to enable static virtual try-on

Launched Sep. 2022
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Computer vision &
Augmented Reality based
iOS, Android, mWeb, dWeb
About Walmart

Walmart is a Fortune 500 American corporation that owns and operates a vast number of super-centers across the US and around the world, alongside a large online presence as a retailer and marketplace.

About Be Your Own model

‘Be Your Own Model’ is an e-commerce tool that lets users virtually try on clothes directly on the product page.

To enable that, users go through an AR and voice-guided image-capturing process.

UX designer

Leena khwaj

Content design

Caitline Brown

UX director

Joe Grinbaum

UX reasercher

Stephanie Gannon

Product

Shivang Patel

Dev team lead

Gireesha Shieony

Overview

Problem and solution

Customer problem

When shopping for apparel online, it is difficult for customers to replicate the in-store experience of trying something on and seeing what it looks like.

While online they are limited to seeing a picture of the item or the item on a model that does not look like them.

Proposed solution

By viewing products on themselves, customers can get more information about the product they are interested in, for intense if the product fits their body shape, whether if it is in their desired length, or if the color is flattering to their skin tone.

Walmart Store

Overview

Market target

280M

Daily online shoppers
in the US in 2023

37M

Daily Walmart customers

12.4M

Daily Walmart apparel customers

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20% of global online customers
Walmart daily market size
From online customers

Pre design

Initial User Study
Before starting to design we conducted an extensive user study to understand the customer mindset, level of trust, and willingness to use the tool.
We send out an email invite to about 24k Walmart apparel customers inviting them to gain early access to the technology and instructing them how to take and upload a photo of themselves. 
Following the user's journey and seeing the images they uploaded was not satisfactory so we knew we had to go back to the drawing board.

This is why we decided that the experience must start with a smart AR camera that will guide our users and auto-photograph them at the right moment according to the requirements.
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Design

User Flow
User Flow
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Design

Entry Point
On the product page, there's a new CTA "Try it on", by clicking, users reveal two options:

Try the clothes on Yourself
Try the clothes on a Model

* We merged the two AR apparel experiences into one CTA to not overload the product page.
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Design

Get ready to pose
The experience starts with a "Get Ready" video instructing users how to get ready to take a successful photo.

Considering this is a self-portrait taken indoors and not in a photo studio, the starting point is not ideal, by following the instructions, customers can be sure their photo will look great and result in successful virtual simulations.

Design

Smart camera, AR-guided flow
Real-time instructions
From our research, we know that most users using this tool will likely be alone and take a picture of themselves with their front camera. The user will put the phone down and step away from it to take the photo.

For that reason, we chose to use voice instructions, accompanied by large text, making sure the user is properly guided step-by-step to the perfect position.
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Device angle detection
The smart camera knows how to recognize the angle at which the camera is placed and guides the user to adjust the camera to the best angle (80-90 degrees).
Body detection
The camera uses computer vision to recognize all body parts and the user's position and movements in real time.
If a correction is needed, the instructions change accordingly, and this is how we guarantee that the picture will be the best.
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User clothing detection
To prevent the user's clothes from showing up in the final simulation, we require them to wear a tank top and shorts. The camera can detect what the user is wearing, and asks them to change if it's needed.
Hair detection
If the user's hair is obstructing their shoulders we ask them to move their hair back.
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Light detection
Lighting is crucial to achieving any good photo, especially one meant for virtual simulations. If the lighting conditions in the room aren't ideal, we'll direct the user to adjust the lighting.
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The countdown
When the camera detects that the user meets all the conditions, it counts down three seconds and takes a picture automatically, so the user can take a picture of Themselves.
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Design

Size indicator 
Our technology only requires the person's height to make the simulations as accurate as possible.As the shooting stage can be frustrating and overwhelming, we wanted to make it as easy as possible. That's why this step comes after the photo is taken.

Design

Processing your photo
We've designed a short animation to keep the user occupied while their image is being processed.
Usually takes 10 seconds.

Design

Review your image
In this step, the user sees their photo in various clothes that we chose to dress them in so they can make sure they're all wearing well before they approve their photo and continue to the item page.

Design

Back to Item page
The user returns back to the item page when he sees themself as a model wearing the garment virtually on their body before he buys it.
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Design

Edit model
From this screen user can see their photo, delete it, or reset their choice back to the original model.
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The desktop does not support taking pictures, but it does support seeing your picture if you took one in the application or if you have selected a model, you can switch between them.
Desktop

Design

Launch

Marketing assets
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Launch

In the Press
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Post-Launch

Quotes from users
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“Totally recommended”

Yes, I really appreciate the personal aspect of being able to actually fit clothing on you from the comfort of your home.

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RestlessCherry3067

49
Female
$50,000 - $69,999
United States

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“Very helpful”

It’s sometimes hard to picture how a clothing item might fit - so the be your own model would help me to get a more accurate idea.

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lawills94

29
Female
$80,000 - $99,999
United States

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Post-Launch

Business impact and stats
Customers engaging with Apparel VTO experience compared to those who do not.
X2
Add to cart
6%
Average Order Value lift
21%
GMV lift
2.4%
Reduction in return rate

Post-Launch

Enhancements 2023

Being first to market means there is no one to look at for learning and inspiration, so after launching our ground breaking VTO tool we conducted multiple user studies and followed analytics closely to learn how might we improve the tool for our users.

Enhancements 

Problem:
The onboarding video is often ignored

Our research found that the customers didn’t understand that this flow results in one photo but thought that they would have to launch the camera on each product they want to try on.

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Enhancements 

Solution:
Break up the video to steps

We chose to divide the video into a few separate steps and change the visual instructions to be more technical and easier to understand.
The instructions are separated into cards, with one instruction on each card.

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Before
After

Enhancements 

Problem:
 Customers don't change to minimal clothing

Customers don’t understand why they need to change their clothes, so they don’t do it before the camera starts.

"It's just too much effort for not enough reward."


       "Tell them before that they're gonna have to wear a tight tank top and tight shorts so they know in advance before they start the process and don't feel like they're stopping in the middle to change their clothes."


                 "…So I did notice that the clothing that I had on, with it being a scoop neck, maybe I need to change the clothes to maybe even less clothing."

Enhancements 

Solution:
Explain why they should change clothes

Listening to our customers, we understand that they would be willing to change their clothes if they understood the reason behind it.
We created this view to show customers what the final result will look like depending on what they wear.

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Before
After

Enhancements 

Problem:
The servers rejects images because of poor lighting

About 20% of images that are sent to the server are rejected due to poor lighting conditions. The smart camera can detect darkness but not at the highest accuracy, so some images get noisy, blurry, or just too dark to process.

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Enhancements 

Solution:
Give a clear explanation before launching the camera

By providing customers with clear instructions before the camera launches, we hope to make the overall process easier and faster, we will disclose more information about the required space and where to place their device.

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Before
After

Enhancements 

Problem:
some customers don’t hear the voice instructions

Enhancements 

Solution:
Add an instruction to turn the volume up

We created a more intuitive, familiar, and understandable level design, based on the iOS internal level tool.

Enhancements 

Problem:
It’s unclear where to place the phone and where to stand

Users seem to get confused when the camera launches and are not sure where to place their device and where exactly they should stand.

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Enhancements 

Solution:
Clear explanation before launching the camera

By providing customers with clear instructions before the camera launches, we hope to make the overall process easier and faster, we will disclose more information about the required space and where to place their device.

Enhancements 

Problem:
The level marker is not clear enough

It’s clear from our user testing that the current design of the yellow level is confusing for many customers, as they seem unsure of what to do at this point.

Enhancements 

Solution:
Redesign the level marker

We created a more intuitive, familiar, and understandable level design, based on the iOS internal level tool.

Design

Style guide
Primary & accent colors
Walmart app Primary action color

#0071DC

#fffffff

Background Color
Drop down
General screen
Tab

#F8F8F8

#fffffff

#E3E4E5

Typography color
Primary
On dark

#2E2F32

#fffffff

Typography color warning
Warning
error
Success Text
Success BG
Warning Text
Warning BG

#662B0D

#9B1419

#2A8703

#EAF3E6

#FFE190

#FFF9E9

Detection & scan lines
Layout grid

#FFF200

#DE1C24

#fffffff

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Typography
Bogle 42 Bogle 36 Bogle 32 Bogle 28 Bogle 24 Bogle 20 Bogle 18 Bogle 16 Bogle 14 Bogle 12.
Shadow
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