Virtual Try-on
78 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
A Simple Strategy for Body Estimation from Partial-View Images
Virtual try-on and product personalization have become increasingly important in modern online shopping, highlighting the need for accurate body measurement estimation.
FashionEngine: Interactive Generation and Editing of 3D Clothed Humans
FashionEngine automates the 3D human production with three key components: 1) A pre-trained 3D human diffusion model that learns to model 3D humans in a semantic UV latent space from 2D image training data, which provides strong priors for diverse generation and editing tasks.
StructLDM: Structured Latent Diffusion for 3D Human Generation
2) A structured 3D-aware auto-decoder that factorizes the global latent space into several semantic body parts parameterized by a set of conditional structured local NeRFs anchored to the body template, which embeds the properties learned from the 2D training data and can be decoded to render view-consistent humans under different poses and clothing styles.
ACDG-VTON: Accurate and Contained Diffusion Generation for Virtual Try-On
Virtual Try-on (VTON) involves generating images of a person wearing selected garments.
Wear-Any-Way: Manipulable Virtual Try-on via Sparse Correspondence Alignment
This paper introduces a novel framework for virtual try-on, termed Wear-Any-Way.
StableGarment: Garment-Centric Generation via Stable Diffusion
In this paper, we introduce StableGarment, a unified framework to tackle garment-centric(GC) generation tasks, including GC text-to-image, controllable GC text-to-image, stylized GC text-to-image, and robust virtual try-on.
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.
