AI试衣终于能一次穿多件，还不用抠图
以前的AI试衣只能换一件单品，还得先抠图。这篇把试衣当成「理解+生成」任务：你给一张模特图、几张衣服照片（甚至别人穿着的街拍），它直接合成模特穿上那些衣服的效果，不用你手动画遮罩。背后是三个阶段的训练——先让模型大量看衣服图，再学怎么穿，最后用两个裁判模型（一个专看衣服细节，一个看整体效果）反复调优。结果：单件试衣效果最好，多件混搭也领先现有系统，还能顺便改姿势。它不是你明天就能用的App，但方向很明确：以后买衣服，拍张自己、选几件衣服，AI直接给你看上身效果。
📄 原文摘要(英文)
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).