Image-based Virtual Try-On (VTON) concerns the synthesis of realistic person imagery through garment re-rendering under human pose and body constraints. In practice, however, existing approaches are typically optimized for specific data conditions, making their deployment reliant on retraining and limiting their generalization as a unified solution. We present OmniVTON++, a training-free VTON framework designed for universal applicability. It addresses the intertwined challenges of garment alignment, human structural coherence, and boundary continuity by coordinating Structured Garment Morphing for correspondence-driven garment adaptation, Principal Pose Guidance for step-wise structural regulation during diffusion sampling, and Continuous Boundary Stitching for boundary-aware refinement, forming a cohesive pipeline without task-specific retraining. Experimental results demonstrate that OmniVTON++ achieves state-of-the-art performance across diverse generalization settings, including cross-dataset and cross-garment-type evaluations, while reliably operating across scenarios and diffusion backbones within a single formulation. In addition to single-garment, single-human cases, the framework supports multi-garment, multi-human, and anime character virtual try-on, expanding the scope of virtual try-on applications. The source code will be released to the public.

翻译：基于图像的虚拟试穿旨在通过人体姿态和身体约束下的服装重渲染，合成逼真的人物图像。然而，在实践中，现有方法通常针对特定数据条件进行优化，导致其部署依赖于重新训练，并限制了其作为统一解决方案的泛化能力。我们提出了OmniVTON++，一个为通用适用性设计的免训练虚拟试穿框架。它通过协调以下三个模块，解决了服装对齐、人体结构连贯性和边界连续性之间相互交织的挑战：用于基于对应关系驱动服装适配的结构化服装变形、用于在扩散采样过程中进行逐步结构调控的主姿态引导、以及用于边界感知细化的连续边界缝合，从而形成一个无需针对特定任务进行重新训练的连贯流程。实验结果表明，OmniVTON++在多样化的泛化设置（包括跨数据集和跨服装类型评估）中均达到了最先进的性能，同时能够基于单一框架，可靠地跨不同场景和扩散主干网络运行。除了单服装、单人物的案例外，该框架还支持多服装、多人物以及动漫角色的虚拟试穿，从而扩展了虚拟试穿的应用范围。源代码将向公众发布。