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
Semantic-Preserved Point-based Human Avatar
To enable realistic experience in AR/VR and digital entertainment, we present the first point-based human avatar model that embodies the entirety expressive range of digital humans.
Image-Based Virtual Try-On: A Survey
In this survey, we provide a comprehensive analysis of the state-of-the-art techniques and methodologies in aspects of pipeline architecture, person representation and key modules such as try-on indication, clothing warping and try-on stage.
Towards Garment Sewing Pattern Reconstruction from a Single Image
In this work, we explore the challenging problem of recovering garment sewing patterns from daily photos for augmenting these applications.
VTON-IT: Virtual Try-On using Image Translation
shuntos/viton-it
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Virtual Try-On (trying clothes virtually) is a promising application of the Generative Adversarial Network (GAN).
FaceChain: A Playground for Human-centric Artificial Intelligence Generated Content
In this paper, we present FaceChain, a personalized portrait generation framework that combines a series of customized image-generation model and a rich set of face-related perceptual understanding models (\eg, face detection, deep face embedding extraction, and facial attribute recognition), to tackle aforementioned challenges and to generate truthful personalized portraits, with only a handful of portrait images as input.
DM-VTON: Distilled Mobile Real-time Virtual Try-On
Additionally, we propose Virtual Try-on-guided Pose for Data Synthesis to address the limited pose variation observed in training images.
Taming the Power of Diffusion Models for High-Quality Virtual Try-On with Appearance Flow
bcmi/DCI-VTON-Virtual-Try-On
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Our approach, namely Diffusion-based Conditional Inpainting for Virtual Try-ON (DCI-VTON), effectively utilizes the power of the diffusion model, and the incorporation of the warping module helps to produce high-quality and realistic virtual try-on results.
AnyDoor: Zero-shot Object-level Image Customization
This work presents AnyDoor, a diffusion-based image generator with the power to teleport target objects to new scenes at user-specified locations in a harmonious way.
TryOnDiffusion: A Tale of Two UNets
fashn-AI/tryondiffusion
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CVPR 2023
Given two images depicting a person and a garment worn by another person, our goal is to generate a visualization of how the garment might look on the input person.
Prompt-Free Diffusion: Taking "Text" out of Text-to-Image Diffusion Models
shi-labs/prompt-free-diffusion
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Text-to-image (T2I) research has grown explosively in the past year, owing to the large-scale pre-trained diffusion models and many emerging personalization and editing approaches.
