Mobile-VTON: High-Fidelity On-Device Virtual Try-On
Zhenchen Wan, Ce Chen, Runqi Lin, Jiaxin Huang, Tianxi Chen, Yanwu Xu, Tongliang Liu, Mingming Gong

TL;DR
Mobile-VTON is a novel on-device virtual try-on framework that achieves high-quality, privacy-preserving garment fitting using a modular architecture and knowledge distillation, enabling practical offline deployment on mobile devices.
Contribution
The paper introduces Mobile-VTON, a fully offline, high-fidelity virtual try-on system with a novel architecture and training strategies optimized for mobile devices, addressing privacy and efficiency.
Findings
Matches or outperforms server-based baselines in quality.
Operates entirely offline on mobile devices.
Maintains high fidelity with low computational cost.
Abstract
Virtual try-on (VTON) has recently achieved impressive visual fidelity, but most existing systems require uploading personal photos to cloud-based GPUs, raising privacy concerns and limiting on-device deployment. To address this, we present Mobile-VTON, a high-quality, privacy-preserving framework that enables fully offline virtual try-on on commodity mobile devices using only a single user image and a garment image. Mobile-VTON introduces a modular TeacherNet-GarmentNet-TryonNet (TGT) architecture that integrates knowledge distillation, garment-conditioned generation, and garment alignment into a unified pipeline optimized for on-device efficiency. Within this framework, we propose a Feature-Guided Adversarial (FGA) Distillation strategy that combines teacher supervision with adversarial learning to better match real-world image distributions. GarmentNet is trained with a…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Face recognition and analysis · Advanced Neural Network Applications
