Voost: A Unified and Scalable Diffusion Transformer for Bidirectional
  Virtual Try-On and Try-Off
Authors: name: Seungyong Lee, name: Jeong-gi Kwak
Organization: NXN Labs

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.