Privacy-Preserving Full-Outfit Virtual Try-On: A Conceptual Architecture For GDPR-Compliant 3D Avatar Systems

Abstract

Background: Full-outfit virtual try-on (VTO) systems aim to digitally render both upper and lower garments on real user images while preserving body shape, pose consistency, garment structure, and visual realism. While substantial progress has been made in single-garment VTO using GAN-based, warping-based, and diffusion-based methods, extending these approaches to full-outfit synthesis introduces additional technical and ethical challenges. These include multi-garment alignment, occlusion handling, structural continuity, and increasing concerns related to user data privacy under frameworks such as the General Data Protection Regulation (GDPR) and UK GDPR.

Objective: This study aims to systematically analyze existing virtual try-on systems with a focus on architectural design, technical limitations, evaluation practices, and privacy considerations, and to propose a conceptual privacy-preserving framework for full-outfit VTO systems.

Methods: A targeted literature review was conducted on representative VTO systems. The analysis examined key architectural paradigms, including warping-based networks, flow-based deformation models, diffusion-based generation, and hybrid 2D–3D pipelines. Technical performance, input requirements, evaluation metrics, and approaches to user data handling were critically assessed.

Results: The review identified persistent technical challenges, including multi-garment misalignment, occlusion inconsistencies, texture distortion, and identity drift. Evaluation practices were found to rely heavily on metrics that inadequately capture full-outfit realism and structural coherence. From a privacy perspective, existing systems demonstrate limited and inconsistent integration of privacy-by-design principles, with notable concerns related to data retention, biometric representation, and re-identification risks.

Conclusion: Full-outfit VTO systems remain constrained by both technical and privacy-related limitations. This study proposes a conceptual privacy-preserving architecture that separates image acquisition from garment simulation through avatar-based processing, enabling data minimisation and reduced reliance on raw user images. The findings highlight the need for future research to integrate robust technical solutions with standardized privacy-aware design principles to support scalable and ethical real-world deployment.