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 code is available at https://github.com/Jerome-Young/OmniVTON-PlusPlus.
ModaFlow: Modality-Aware Flow Matching for High-Fidelity Virtual Try-On
South China University of Technology（华南理工大学）；Universitat de Barcelona（巴塞罗那大学）；Computer Vision Center（计算机视觉中心）；Guangdong Provincial Key Laboratory of Multimodal Big Data Intelligent Analysis（广东省多模态大数据智能分析重点实验室）
RefTon: Reference person shot assist virtual Try-on
The University of Tokyo（东京大学）；AI Research（360AI研究院）；University of California, San Diego（加州大学圣地亚哥分校）；The Hong Kong University of Science and Technology（香港科学与技术大学）
MOFA-VTON: More Fashion Possibilities with Fine-Grained Adaptations in Virtual Try-On
Harbin Institute of Technology（哈尔滨工业大学）；HiDream.ai Inc.（HiDream.ai公司）；Harbin Institute of Technology (Weihai) Qingdao Research Institute（哈尔滨工业大学（威海）青岛研究院）
Undress to Redress: A Training-Free Framework for Virtual Try-On
Zhiying Li 1（李子莹1）；Junhao Wu 3（吴俊浩3）；Yeying Jin 4（金悦莹4）；Daiheng Gao 5（高代恒5）；Yun Ji 2（纪云2）；Kaichuan Kong 1（孔开传1）；Lei Yu 6（于磊6）；Hao Xu 7（徐浩7）；Kai Chen 7（陈凯7）；Bruce Gu 8（古布鲁斯8）；Nana Wang 9（王娜9）；Zhaoxin Fan 2（范朝新2）
PEMF-VTO: Point-Enhanced Video Virtual Try-on via Mask-free Paradigm
School of Information Science and Technology, University of Science and Technology of China（信息科学与技术学院，中国科学技术大学）；School of Electrical and Information Engineering, Tianjin University（电气与信息工程学院，天津大学）；Alibaba International Digital Commerce Group（阿里巴巴国际数字商业集团）
Spatio-Temporal Garment Reconstruction Using Diffusion Mapping via Pattern Coordinates
CVLab, École Polytechnique Fédérale de Lausanne (EPFL)（瑞士联邦理工学院（EPFL）计算机视觉实验室）；Mohamed bin Zayed University of Artificial Intelligence（马尔代夫人工智能大学）；Department of Computer Science and Engineering, Southern University of Science and Technology（南方科技大学计算机科学与工程系）；Pinscreen and Mohamed bin Zayed University of Artificial Intelligence（Pinscreen和马尔代夫人工智能大学）
