Voost：面向双向虚拟试穿与试脱的统一可扩展扩散变换器

摘要

虛擬試穿技術旨在合成人物穿著目標服裝的真實圖像，然而精確建模服裝與人體之間的對應關係仍是一大挑戰，尤其是在姿態和外觀變化的情況下。本文提出Voost——一個統一且可擴展的框架，通過單一擴散變壓器聯合學習虛擬試穿與試脫。通過共同建模這兩項任務，Voost使得每對服裝與人體能夠相互監督，並支持對生成方向及服裝類別的靈活條件控制，從而增強了服裝與人體關係的推理能力，無需特定任務網絡、輔助損失或額外標籤。此外，我們引入了兩種推理時技術：注意力溫度縮放以增強對分辨率或遮罩變化的魯棒性，以及自我校正採樣，該技術利用任務間的雙向一致性。大量實驗表明，Voost在試穿與試脫基準測試中均達到了最先進的成果，在對齊精度、視覺逼真度及泛化能力上持續超越強勁基線。

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：面向双向虚拟试穿与试脱的统一可扩展扩散变换器

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

摘要

Support