One of the major challenges our developers faced during developing virtual try-on clothes solutions is creating a setup that can measure body and estimate body type accurately to suggest an appropriate apparel size.
Another obstacle that comes up with creating a virtual try-on clothes platform is designing the 3D model of the apparel in the inventory. Outfit stores provide a comprehensive range of clothing for both women and men, and maintaining the 3D model inventory of these is a hell of a task as new arrivals hit the stores now and then.
With the development of Virtual try-on clothes solutions, there is hardcore hardware dependency. It is impossible to create such solutions providing the facility of virtual try-on with a particular set of hardware components.
For body measurement and estimating body type, the perfect solution our skilled developers come up with is the creation of a deep learning model. A prototype that not only identifies basic key points on the human body’s joint but also the body shape in 3D to offer a proper AR experience.
To reduce the efforts and time of preparing 3D models of each apparel, our developers came up with the solution of transferring 2D images of the clothes over the prepared 3D models for the different styling patterns, body measurement and body type. This ends up the need of maintaining a vast inventory of 3D models of distinct apparel, which results in resource and time-saving to a great extent.
There is no solution for hardware dependency. The only way to overcome this obstacle is to gather all the required hardware for the platform development, and our developers did the same.
The NineHertz incorporated Virtual Try-on Clothes Solution with the listed below features
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