AI RESEARCH
DirectTryOn: One-Step Virtual Try-On via Straightened Conditional Transport
arXiv CS.CV
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ArXi:2605.12939v1 Announce Type: new Recent diffusion- and flow-based VTON methods achieve strong results with pretrained generative models, but their reliance on multi-step sampling incurs high inference cost, while existing acceleration methods largely overlook the intrinsic structure of the try-on task. In this paper, we highlight a key observation: VTON outputs are highly constrained by the conditional inputs, suggesting that the conditional sampling trajectory can be much straighter than that in general image generation, making one-step generation a natural solution.