Virtual Dressing Room Using Depth Sensor Gesture Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Online clothing shoppers face the challenge of not being able to try on clothing virtually before making a purchase, which can lead to uncertainty about fit and style, and existing solutions do not effectively address this need for a seamless virtual try-on experience.
Innovation Solution
A system utilizing depth sensors, such as the Microsoft Kinect, and gesture recognition technology allows users to interact with a virtual dressing room by mapping virtual clothing items onto their avatar, enabling them to try on clothing virtually and perform actions like purchasing or listing items for sale using gestures and voice commands, regardless of their location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users shop for clothing online without virtual try-on capability, then the shopping process is simple and fast, but users cannot assess fit and style leading to purchase uncertainty
Solution Approach 1:
The system creates a digital copy (avatar) of the user based on spatial data, and maps virtual clothing items onto this copy. This allows users to visualize how clothing fits without physically trying it on, thereby improving purchase confidence while keeping the system relatively simple by using virtual replicas instead of physical interactions.
Solution Approach 2:
The patent introduces a depth sensor (e.g., Kinect) as an intermediary device that captures spatial data of the user and translates it into actionable information for virtual try-on. This intermediary enables the connection between the physical user and the virtual clothing system, improving reliability without requiring complex direct interactions.
2Ease of operation
If physical try-on is required to assess clothing fit, then accurate fit assessment is achieved, but the shopping process becomes time-consuming and location-dependent
Solution Approach 1:
The system replaces the mechanical action of physically trying on clothes with a digital/spatial-based virtual try-on process. The depth sensor captures the user's spatial form, and virtual clothing is mapped onto this digital model, eliminating the need for physical manipulation of clothing items and significantly reducing time requirements.
Solution Approach 2:
The system performs preliminary spatial scanning of the user to create an accurate digital model before the actual clothing selection process. This preliminary action captures all necessary measurement data in advance, allowing rapid virtual try-on of multiple clothing items without repeated physical measurements, thereby reducing overall shopping time.
3Adaptability or versatility
If traditional e-commerce interfaces are used, then the user interface is simple, but user engagement and interaction are limited
Solution Approach 1:
The system transitions from static traditional e-commerce interfaces to dynamic gesture-based interactions. Users can manipulate virtual clothing items, rotate avatars, and select options through natural hand gestures captured by the depth sensor, making the interface adaptable to user movements while maintaining relative simplicity through intuitive physical motions.
Solution Approach 2:
The depth sensor serves multiple functions: capturing spatial data for avatar creation, detecting gestures for interaction, and providing depth information for realistic clothing mapping. This multi-functional approach enhances interaction capability without requiring multiple separate devices or overly complex interface mechanisms.
Data Source
AI summary
A method and system are provided to facilitate recognition of gestures representing commands to initiate actions within an electronic marketplace on behalf of a user. Spatial data about an environment external to a depth sensor may be received by an action machine. The action machine may generate a first model of a body of the user based on a first set of spatial data received at a first time. The action machine may then generate a second model of the body of the user based on a second set of spatial data received at a second time. The action machine may further determine that a detected difference between the first and second models corresponds to a gesture by the user, and that this gesture represents a command by the user to initiate an action within the electronic marketplace on behalf of the user.