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

• URL: http://arxiv.org/abs/2508.04825v1
• Date: Wed, 06 Aug 2025 19:10:58 GMT
• Title: Voost: A Unified and Scalable Diffusion Transformer for Bidirectional Virtual Try-On and Try-Off
• Authors: Seungyong Lee, Jeong-gi Kwak,
• Abstract summary: We propose Voost - a unified framework that jointly learns virtual try-on and try-off with a single diffusion transformer.<n>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.
• Score: 9.45991209383675
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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