Unified virtual try-on system works for many cloth and person scenarios
FlexTry-On: A Time-Efficient Framework for Multi-Scenario Virtual Try-on
Computer Vision and Pattern Recognition
Summary
Virtual try-on technology lets people see how clothes look on them without physically wearing them. Most systems only work for one type of situation, like putting clothes on a single person or swapping outfits between two people. The authors created FlexTry-On, a single system that can handle many different types of virtual try-on tasks quickly and without needing separate setups. This system uses a smart method to understand clothes and people together and works well on tests for different cases. It can even handle new situations it hasn't seen before without extra changes.
What this means in practice
- •For clothing retailers: Offer customers fast and flexible virtual try-on options for various clothing and person styles using one system.$Commercial implications: Enables a multi-scenario virtual try-on product appealing to diverse customers with reduced technical complexity.
- •For mobile app developers: Integrate a lightweight virtual try-on feature that works across diverse clothing and user scenarios in fashion-related apps.
Authors
Phuong Dam, Quyen Nguyen Duc, Son Hoang Minh, Daeyoung Kim
Abstract
Virtual try-on has achieved remarkable progress in recent years, yet most existing methods are designed for a specific scenario, such as cloth-to-person or person-to-person try-on, and often require separate conditioning strategies and processing pipelines for different settings. We present FlexTry-On, a unified and time-efficient diffusion-based framework for diverse virtual try-on scenarios, including cloth-to-person, person-to-person, cross-domain, and in-the-wild settings. FlexTry-On adopts a common conditioning formulation, where a text-aware condition extraction strategy converts heterogeneous garment sources into a unified representation, and a dual-conditioning diffusion transformer jointly models garment appearance and person identity. Extensive experiments demonstrate competitive performance on both cloth-to-person and person-to-person benchmarks while maintaining fast inference. Moreover, the same framework can be directly applied to unseen cross-domain scenarios without requiring separate architectures or scenario-specific processing pipelines. These results highlight the potential of unified and efficient virtual try-on frameworks as a practical step toward more flexible and widely applicable virtual try-on systems.