Oxygen-TryOn: Fashion-Native Foundation Model for Any-item Virtual Try-On
Oxygen-TryOn is a unified foundation model for any-item virtual try-on, built specifically for the task rather than adapted from general-purpose image editors. It leverages a dedicated data engine and try-on-specific training to generate photorealistic images of a subject wearing reference items across diverse fashion categories. Unlike prior systems limited to single garments or garment-centric approaches, Oxygen-TryOn supports full/half-body views, variable references, and free multi-item composition while preserving identity and appearance. It employs a three-stage training recipe (CPT, SFT, RL) with a hybrid reward model. On public benchmarks and its internal bench, it achieves state-of-the-art single-item try-on and leads on multi-item, matching or surpassing leading proprietary and open-source systems.
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[Submitted on 23 Jul 2026]
Title:Oxygen-TryOn: Fashion-Native Foundation Model for Any-item Virtual Try-On
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Abstract:We present Oxygen-TryOn, a unified foundation model for any-item virtual try-on. Rather than repurposing a general-purpose image editor, Oxygen-TryOn is fashion-native, built for try-on through a dedicated data engine and try-on-specific training. Given one or more reference items (clean product shots or in-the-wild worn-on photos) and a single target subject image, it synthesizes a photorealistic image of the subject wearing the items across virtually any fashion category. Prior systems handle a single garment category in a studio setting, and recent multi-reference methods remain garment-centric; in contrast, Oxygen-TryOn supports diverse items and scenarios, including full- and half-body views, a variable number of references, and free multi-item composition, while faithfully preserving both subject identity and item appearance. Instead of mask-based inpainting, we reformulate try-on as a multi-reference, understanding-driven generation task. We build a data engine that collects, manufactures, annotates, and filters high-quality try-on data at scale, and design a three-stage recipe of continued pre-training (CPT), supervised fine-tuning (SFT), and reinforcement learning (RL). The RL stage uses a hybrid reward combining an in-house try-on reward model with a proprietary, rubric-guided general-purpose model, jointly supervising fine-grained consistency and instruction-level quality. It also follows general editing instructions (e.g., pose changes) in the same pass. Across public benchmarks and our in-house Oxygen-TryOn Bench, it achieves state-of-the-art consistency and realism on single-item try-on and leads on multi-item try-on, matching or surpassing both leading proprietary systems (Nano Banana Pro, GPT-Image-2, Seedream5 Lite) and open-source models (FLUX.2).
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.21694 [cs.CV]
(or arXiv:2607.21694v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.21694
arXiv-issued DOI via DataCite
Submission history
From: Yong Liu [view email] [v1] Thu, 23 Jul 2026 17:45:56 UTC (44,509 KB)
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