Voost: A Unified and Scalable Diffusion Transformer for Bidirectional
  Virtual Try-On and Try-Off
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.
6895558948b0ae5ca2710cf7
Seungyong Lee
claimed_verified
2025-08-08T16:20:16.112Z
660eb276a0de79aa07e754df
/avatars/48ee2d484e70714516589a8b13137036.svg
Seungyong Lee
user
RyanL22
6895558948b0ae5ca2710cf8
Jeong-gi Kwak
claimed_verified
2025-08-08T16:20:12.557Z
635035fedc10894a3f66ea79
/avatars/bd9c373705d29ad8f14987a2864e5956.svg
Jeong-gi Kwak
user
jgkwak
Voost: A Unified and Scalable Diffusion Transformer for Bidirectional
  Virtual Try-On and Try-Off
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, a unified diffusion transformer framework, jointly learns virtual try-on and try-off, enhancing garment-body correspondence and achieving state-of-the-art results across benchmarks.
diffusion transformer garment-body correspondence virtual try-on virtual try-off attention temperature scaling self-corrective sampling bidirectional consistency