JewelTry: Mask-Free Scale Aware Jewelry Virtual Try-On

2026-09-15

AI Summary

为了解决珠宝虚拟试戴中的尺寸和位置准确性问题，本文提出了一种无需遮罩的扩散框架JewelTry，并引入了JVTO-Bench基准数据集以提供规模忠实度支持。

Abstract

Virtual try-on (VTON) enables customers to visualize how fashion products appear when worn and has become an important technology for online shopping. While recent advances have substantially improved garment VTON, jewelry remains a challenging and underexplored category due to its small size, rigid structure, and sensitivity to fine-grained visual details. Realistic jewelry VTON requires not only faithful appearance transfer but also accurate scale and placement relative to the wearer. Existing jewelry VTON methods typically rely on mask guidance, whereas mask-free approaches lack explicit guidance for modeling the product scale. To bridge this gap, we introduce JVTO-Bench, a benchmark dataset for scale-faithful jewelry VTON, providing reference source target triplets with real-world product-scale annotations across four major jewelry categories. Building upon this benchmark, we propose JewelTry, a mask-free diffusion framework for scale-aware jewelry VTON. JewelTry incorporates a scale adapter that encodes product dimensions into a scale token, enabling the model to learn scale relationships between jewelry items and surrounding human anatomy in-context. To further improve jewelry consistency, we introduce a single-directional condition attention mechanism and an attention refinement loss that preserve both coarse geometry and fine-grained structural details of the reference jewelry. Extensive experiments show that JewelTry achieves a balance among visual fidelity, background preservation, object consistency and scale accuracy, establishing a strong baseline for mask-free, scale-aware jewelry virtual try-on.

Problem

• Virtual Try-On
• Jewelry
• Scale-Aware
• Mask-Free
• Product Scale

Innovation

• mask-free diffusion framework
• scale adapter
• single-directional condition attention mechanism
• attention refinement loss