Virtual Try-on
79 papers with code • 7 benchmarks • 11 datasets
Virtual try-on of clothing or other items such as glasses and makeup. Most recent techniques use Generative Adversarial Networks.
Benchmarks
These leaderboards are used to track progress in Virtual Try-on
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Libraries
Use these libraries to find Virtual Try-on models and implementationsDatasets
DeepFashion
VITON
VITON-HD
Deep Fashion3D
Dress Code
MoVi
STRAT
Latest papers with no code
Better Fit: Accommodate Variations in Clothing Types for Virtual Try-on
Image-based virtual try-on aims to transfer target in-shop clothing to a dressed model image, the objectives of which are totally taking off original clothing while preserving the contents outside of the try-on area, naturally wearing target clothing and correctly inpainting the gap between target clothing and original clothing.
Time-Efficient and Identity-Consistent Virtual Try-On Using A Variant of Altered Diffusion Models
This study discusses the critical issues of Virtual Try-On in contemporary e-commerce and the prospective metaverse, emphasizing the challenges of preserving intricate texture details and distinctive features of the target person and the clothes in various scenarios, such as clothing texture and identity characteristics like tattoos or accessories.
PFDM: Parser-Free Virtual Try-on via Diffusion Model
Virtual try-on can significantly improve the garment shopping experiences in both online and in-store scenarios, attracting broad interest in computer vision.
Mobile Fitting Room: On-device Virtual Try-on via Diffusion Models
The growing digital landscape of fashion e-commerce calls for interactive and user-friendly interfaces for virtually trying on clothes.
DressCode: Autoregressively Sewing and Generating Garments from Text Guidance
We first introduce SewingGPT, a GPT-based architecture integrating cross-attention with text-conditioned embedding to generate sewing patterns with text guidance.
A Novel Garment Transfer Method Supervised by Distilled Knowledge of Virtual Try-on Model
This paper proposes a novel garment transfer method supervised with knowledge distillation from virtual try-on.
Product-Level Try-on: Characteristics-preserving Try-on with Realistic Clothes Shading and Wrinkles
Image-based virtual try-on systems, which fit new garments onto human portraits, are gaining research attention. An ideal pipeline should preserve the static features of clothes(like textures and logos)while also generating dynamic elements(e. g. shadows, folds)that adapt to the model's pose and environment. Previous works fail specifically in generating dynamic features, as they preserve the warped in-shop clothes trivially with predicted an alpha mask by composition. To break the dilemma of over-preserving and textures losses, we propose a novel diffusion-based Product-level virtual try-on pipeline,\ie PLTON, which can preserve the fine details of logos and embroideries while producing realistic clothes shading and wrinkles. The main insights are in three folds:1)Adaptive Dynamic Rendering:We take a pre-trained diffusion model as a generative prior and tame it with image features, training a dynamic extractor from scratch to generate dynamic tokens that preserve high-fidelity semantic information.
3D Landmark Detection on Human Point Clouds: A Benchmark and A Dual Cascade Point Transformer Framework
Comparative evaluations with popular point-based methods on HPoint103 and the public dataset DHP19 demonstrate the dramatic outperformance of our D-CPT.
Dress-Me-Up: A Dataset & Method for Self-Supervised 3D Garment Retargeting
We propose a novel self-supervised framework for retargeting non-parameterized 3D garments onto 3D human avatars of arbitrary shapes and poses, enabling 3D virtual try-on (VTON).
Significance of Anatomical Constraints in Virtual Try-On
In general, a VTON system takes a clothing source and a person's image to predict the try-on output of the person in the given clothing.
