Voost：一种统一且可扩展的扩散Transformer，用于双向虚拟试穿与试脱

摘要

虚拟试衣技术旨在合成人物穿着目标服装的真实图像，但精确建模服装与人体间的对应关系仍是一个持续挑战，尤其在姿态和外观变化的情况下。本文提出Voost——一个统一且可扩展的框架，通过单一扩散变换器联合学习虚拟试穿与试脱。通过共同建模这两项任务，Voost使得每对服装-人物都能双向监督，并支持对生成方向及服装类别的灵活条件控制，从而增强服装与人体间的关系推理，无需特定任务网络、辅助损失或额外标签。此外，我们引入了两项推理时技术：注意力温度缩放以应对分辨率或掩码变化的鲁棒性，以及自校正采样，该技术利用任务间的双向一致性。大量实验表明，Voost在试穿与试脱基准测试中均达到了最先进的成果，在对齐精度、视觉真实感及泛化能力上持续超越强基线模型。

English

Virtual try-on aims to synthesize a realistic image of a person wearing a target garment, but accurately modeling garment-body correspondence remains a persistent challenge, especially under pose and appearance variation. In this paper, we propose Voost - a unified and scalable framework that jointly learns virtual try-on and try-off with a single diffusion transformer. By modeling both tasks jointly, Voost enables each garment-person pair to supervise both directions and supports flexible conditioning over generation direction and garment category, enhancing garment-body relational reasoning without task-specific networks, auxiliary losses, or additional labels. In addition, we introduce two inference-time techniques: attention temperature scaling for robustness to resolution or mask variation, and self-corrective sampling that leverages bidirectional consistency between tasks. Extensive experiments demonstrate that Voost achieves state-of-the-art results on both try-on and try-off benchmarks, consistently outperforming strong baselines in alignment accuracy, visual fidelity, and generalization.

Voost：一种统一且可扩展的扩散Transformer，用于双向虚拟试穿与试脱

Voost: A Unified and Scalable Diffusion Transformer for Bidirectional Virtual Try-On and Try-Off

摘要

Support