OmniVTON++: Training-Free Universal Virtual Try-On with Principal Pose Guidance

2026-02-16

AI Summary

Existing virtual try-on methods typically rely on domain-specific data or require retraining, limiting their generalizability and practical deployment. This work proposes the first training-free, general-purpose virtual try-on framework that simultaneously achieves garment alignment, human structural consistency, and boundary continuity during diffusion sampling through structured clothing deformation, pose-guided warping, and boundary-aware seamless stitching. The method is compatible with various diffusion model backbones and demonstrates strong generalization across diverse scenarios—including cross-dataset transfer, multiple garment types, multi-person try-on, and even animated characters—consistently achieving state-of-the-art performance under multiple generalization settings. This significantly enhances the practicality and robustness of virtual try-on systems.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

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.

Problem

• Virtual Try-On
• Generalization
• Training-Free
• Cross-Dataset
• Multi-Human

Innovation

• Training-Free
• Virtual Try-On
• Principal Pose Guidance
• Structured Garment Morphing
• Diffusion Model