OmniVTON: Training-Free Universal Virtual Try-On
Zhaotong Yang, Yuhui Li, Shengfeng He, Xinzhe Li, Yangyang Xu, Junyu Dong, Yong Du

TL;DR
OmniVTON is a novel training-free framework for virtual try-on that achieves high-quality, multi-person garment transfer across diverse scenarios by disentangling garment and pose conditioning without requiring model training.
Contribution
It introduces a training-free, universal VTON method that effectively handles multiple conditions and multi-human scenarios by decoupling garment and pose constraints.
Findings
Achieves superior results across various datasets and garment types.
First framework capable of multi-human virtual try-on in a single scene.
Demonstrates high texture fidelity and pose accuracy without training.
Abstract
Image-based Virtual Try-On (VTON) techniques rely on either supervised in-shop approaches, which ensure high fidelity but struggle with cross-domain generalization, or unsupervised in-the-wild methods, which improve adaptability but remain constrained by data biases and limited universality. A unified, training-free solution that works across both scenarios remains an open challenge. We propose OmniVTON, the first training-free universal VTON framework that decouples garment and pose conditioning to achieve both texture fidelity and pose consistency across diverse settings. To preserve garment details, we introduce a garment prior generation mechanism that aligns clothing with the body, followed by continuous boundary stitching technique to achieve fine-grained texture retention. For precise pose alignment, we utilize DDIM inversion to capture structural cues while suppressing texture…
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