Fashion-Native FoundationModel for Any-Item Virtual Try-On

We present Oxygen-TryOn, a unified foundation model for any-item virtual try-on. Rather than repurposing a general-purpose image editor through prompting, which tends to hallucinate garment details, drift on subject identity, and break down on fine-grained texture, Oxygen-TryOn is fashion-native: it is purpose-built for try-on, powered by a dedicated data engine and try-on-specific training. Given one or more reference items, provided either as clean product shots or as in-the-wild photos of someone already wearing them, together with a single target subject image, the model synthesizes a photorealistic image of that subject wearing the referenced items, spanning virtually any fashion category: clothing, outerwear, accessories, footwear, bags, and beyond. Most prior systems handle only a single garment category in a controlled studio setting, and even recent multi-reference methods remain garment-centric. In contrast, Oxygen-TryOn supports diverse items and real-world scenarios, including full-body and half-body views, a variable number of references, and free multi-item composition, while faithfully preserving both the subject’s identity and each reference item’s fine-grained appearance, from textures and prints to logos and structural silhouettes. Instead of casting try-on as mask-based inpainting, we reformulate it as a multi-reference, understanding-driven generation task: the model reasons about what each item is and how it should be worn, handling occlusion, deformation, and layering rather than merely filling a predefined region, which in turn yields stronger generalization to unseen items and compositions. To unlock this capability, we build a dedicated data engine that collects, manufactures, annotates, and filters high-quality try-on data at scale, and we design a three-stage training recipe comprising continued pre-training (CPT), large-scale supervised fine-tuning (SFT), and reinforcement learning (RL). The RL stage is driven by a hybrid reward mechanism that integrates an in-house try-on reward model with a proprietary, rubric-guided general-purpose model, jointly supervising fine-grained consistency and instruction-level quality. As a by-product, the same model still follows general editing instructions (e.g., pose changes) within a single pass, with no task switching. Across public benchmarks and our in-house Oxygen-TryOn Bench, Oxygen-TryOn 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 (e.g., Nano Banana Pro, GPT-Image-2, Seedream5 Lite) and the strongest open-source models (e.g., FLUX.2). To the best of our knowledge, it is the first system to deliver any-item, multi-reference try-on at this level of fidelity. We further detail the recipe behind the model, namely the data engine and the CPT–SFT–RL pipeline, to make our design choices transparent and reusable.