GlamTry: Advancing Virtual Try-On for High-End Accessories
This addresses a gap in online retail for accessories, but it is incremental as it extends existing methods to a new domain.
The paper tackled the lack of photorealistic virtual try-on models for accessories like jewelry and watches by customizing and retraining existing clothing-based models, resulting in improved location prediction even with a small dataset.
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.