AI试衣终于能一次穿多件，还不用抠图
以前的AI试衣只能一件一件来，还得用面具遮住原图再补。这篇直接把试衣当成「理解+生成」：你给几张衣服照片（甚至别人穿着的也行），它一次全穿上，还能保持你本人长相和衣服细节。背后是专门为试衣造的数据引擎和三阶段训练（预训练+微调+强化学习），最后一步用两个奖励模型同时盯着「衣服细节像不像」和「整体效果好不好」。在公开测试里，单件和多件试衣都超过了现有最好的商用系统。它不是你明天就能用的，但方向很明确：以后网购不用再脑补衣服上身效果了。
📄 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).