Virtual Try-On-Enhancing Fashion Exploration for Gen-Z

Abstract

Virtual Try- On( VTON) is a fleetly arising technology designed to digitally fantasize how garments might appear when worn by individualities, therefore transubstantiating traditional fashion and retail gests . This check paper strictly explores colorful state- of- the- art ways employed in VTON systems, primarily fastening on image- grounded, 3D- grounded, and cold-blooded approaches. originally, the paper introduces abecedarian generalities of virtual pass- on systems, tracing their literal progression and applicability within ultramoderne-commerce and fashion diligence. It totally categorizes methodologies into distinct groups, pressing introducing approaches similar as screwing styles, generative inimical networks( GAN), and advanced 3D garment simulation ways. The paper further emphasizes pivotal technologies and datasets vital to VTON advancements, including GANs, mills, prolixity models, and benchmarking datasets like DeepFashion and VITON. In addressing being limitations, this check underscores critical challenges similar as achieving photorealistic picture, effectively handling occlusions and different mortal acts, icing real- time processing, and generalizing across colorful fabric textures and garment styles. also, recent inventions and their counteraccusations on marketable and real- time operations are completely bandied. Eventually, the paper delineates unborn exploration directions aimed at enhancing system literalism, scalability, personalization, and integration of arising generative AI methodologies, pressing the significant eventuality for uninterrupted invention and operation in the digital retail geography.

Introduction

The rapid growth of e-commerce and fashion demands innovative technologies to enhance online shopping experiences. Virtual Try-On (VTON) systems have emerged as key tools, enabling consumers to virtually try clothing and reducing uncertainties in online garment purchases. Initially, VTON relied on basic 2D image processing, but advancements in AI, particularly Generative Adversarial Networks (GANs), have greatly improved realism, texture preservation, and garment alignment. More recently, 3D modeling and simulation have enhanced fit accuracy and fabric dynamics, although these methods are computationally intensive.

Several notable VTON methods exist, including:

• 2D image-based approaches (e.g., VITON, CP-VTON) focus on warping garments onto images with GAN-based refinement.

• 3D model-based approaches simulate realistic garment draping and fabric behavior but require heavy computation.

• Hybrid methods combine 2D and 3D techniques for improved realism and efficiency.

Key challenges remain, such as achieving photorealism, handling diverse poses and occlusions, maintaining real-time performance, and generalizing across garment types. Emerging solutions include transformer models for better spatial understanding and diffusion models for superior image quality. Optimizations like pruning and quantization help address real-time constraints.

Future research should emphasize improving photorealism and scalability, developing few-shot or tone-learning models for adaptability, integrating multi-modal AI (e.g., natural language interaction), and enabling cross-platform VTON in AR, e-commerce, and metaverse environments. Ethical considerations like reducing bias and increasing inclusivity are also crucial for advancing VTON technologies.

References

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Copyright

Copyright © 2025 Iban Hussain, Dr. Nidhi Sharma. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.