DirectTryOn achieves state-of-the-art one-step virtual try-on performance by applying pure conditional transport, garment preservation loss, and self-consistency loss to straighten trajectories in pretrained generative models.
Tryoffanyone: Tiled cloth generation from a dressed person
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Dress-ED is the first large-scale benchmark unifying virtual try-on, try-off, and text-guided garment editing with 146k verified samples plus a multimodal diffusion baseline.
A Dual-UNet diffusion model for virtual garment reconstruction from clothed images sets new benchmarks on VITON-HD and DressCode by optimizing Stable Diffusion variants, mask conditioning, and auxiliary losses.
ImgEdit supplies 1.2 million curated edit pairs and a three-part benchmark that let a VLM-based model outperform prior open-source editors on adherence, quality, and detail preservation.
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DirectTryOn: One-Step Virtual Try-On via Straightened Conditional Transport
DirectTryOn achieves state-of-the-art one-step virtual try-on performance by applying pure conditional transport, garment preservation loss, and self-consistency loss to straighten trajectories in pretrained generative models.
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Dress-ED: Instruction-Guided Editing for Virtual Try-On and Try-Off
Dress-ED is the first large-scale benchmark unifying virtual try-on, try-off, and text-guided garment editing with 146k verified samples plus a multimodal diffusion baseline.
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What Matters in Virtual Try-Off? Dual-UNet Diffusion Model For Garment Reconstruction
A Dual-UNet diffusion model for virtual garment reconstruction from clothed images sets new benchmarks on VITON-HD and DressCode by optimizing Stable Diffusion variants, mask conditioning, and auxiliary losses.
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ImgEdit: A Unified Image Editing Dataset and Benchmark
ImgEdit supplies 1.2 million curated edit pairs and a three-part benchmark that let a VLM-based model outperform prior open-source editors on adherence, quality, and detail preservation.