GlamTry: Advancing Virtual Try-On for High-End Accessories

TL;DR

This paper introduces GlamTry, a novel virtual try-on system for high-end accessories like jewelry and watches, adapting clothing try-on techniques and computer vision models to improve accessory fitting accuracy.

Contribution

It extends virtual try-on technology from clothing to accessories by customizing and retraining models with accessory-specific data and architecture modifications.

Findings

Improved location prediction accuracy over clothing models

Potential scalability with larger datasets exceeding 10,000 images

Demonstrates feasibility of virtual accessory try-on applications

Abstract

The paper aims to address the lack of photorealistic virtual try-on models for accessories such as jewelry and watches, which are particularly relevant for online retail applications. While existing virtual try-on models focus primarily on clothing items, there is a gap in the market for accessories. This research explores the application of techniques from 2D virtual try-on models for clothing, such as VITON-HD, and integrates them with other computer vision models, notably MediaPipe Hand Landmarker. Drawing on existing literature, the study customizes and retrains a unique model using accessory-specific data and network architecture modifications to assess the feasibility of extending virtual try-on technology to accessories. Results demonstrate improved location prediction compared to the original model for clothes, even with a small dataset. This underscores the model's potential with larger datasets exceeding 10,000 images, paving the way for future research in virtual accessory try-on applications.