发表机构
State Key Lab of CAD & CG, Zhejiang University; Style3D Research China（浙江大学计算机辅助设计与图形学国家重点实验室; 中国凌迪科技风格实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
TAMF-VTON旨在解决虚拟试穿方法局限，通过统一生成管道，含轻量级专家混合适应方案、频域监督机制和强大数据管理管道，实现无掩码、多服装组合、纹理保留及高效推理，优于现有方法，为数字时尚提供可行方案。
AI 中文摘要
近期基于扩散的虚拟试穿（VTON）方法存在局限，依赖分割掩码、细粒度纹理保留不足、对任意多服装组合支持有限，在实际电子商务部署中面临挑战。我们提出TAMF-VTON，一个纹理感知、无掩码框架，能在实际无约束条件下实现高保真图像合成。推理时无需人工解析或修复掩码，支持多样服装风格等，通过统一生成管道实现，含三个关键组件。实验表明该方法在定量指标和感知质量上优于现有方法，在消费硬件上高效推理，为现实数字时尚场景提供了商业可行的可扩展部署方案。
英文摘要
Recent diffusion-based virtual try-on (VTON) methods remain limited by their reliance on segmentation masks, insufficient preservation of fine-grained textures, and limited support for arbitrary multi-garment compositions. Consequently, existing approaches still face significant challenges in real-world e-commerce deployment. We present TAMF-VTON, a texture-aware, mask-free framework that enables high-fidelity image synthesis under practical unconstrained conditions. Our method requires no human parsing or inpainting masks at inference time and supports diverse garment styles, categories, and quantities, enabling the simultaneous transfer of multiple items while preserving body structure and intricate texture details. This is achieved through a unified generative pipeline with three key components: (1) a lightweight Mixture-of-Experts (MoE) adaptation scheme that enables efficient fine-tuning without compromising the base model's general editing capabilities; (2) a frequency-domain supervision mechanism that explicitly optimizes high-frequency spectral consistency to preserve high-fidelity textures; and (3) a robust data curation pipeline employing an adaptive inpainting strategy to simulate the inverse VTON process for high-quality training pair generation. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods in both quantitative metrics and perceptual quality. Optimized for efficiency, the model achieves inference in under 15 seconds per image on an NVIDIA RTX 4090 with INT4 quantization. By combining mask-free operation, flexible multi-garment composition, faithful texture preservation, and efficient inference on consumer hardware, TAMF-VTON demonstrates a commercially viable solution for scalable deployment in real-world digital fashion scenarios. The project is available at https://www.style3d.ai/ai-photoshoot/virtual-clothing-try-on.
