Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

897
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
897

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

SynergyGraph: predicting cell line specific drug combination synergy scores using knowledge graph representation and hypergraph modeling.

Scientific reports·2025
Same author

SynergyImage: image-based model for drug combinations synergy score prediction.

BMC bioinformatics·2025
Same author

Drug Repurposing Using Hypergraph Embedding Based on Common Therapeutic Targets of a Drug.

Journal of computational biology : a journal of computational molecular cell biology·2024
Same author

Prediction of drug-drug interaction events using graph neural networks based feature extraction.

Scientific reports·2022
Same author

Detection of polypharmacy side effects by integrating multiple data sources and convolutional neural networks.

Molecular diversity·2022
Same author

BiCAMWI: A Genetic-Based Biclustering Algorithm for Detecting Dynamic Protein Complexes.

PloS one·2016
Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026
Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026
Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026
Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026
Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026
Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Sep 9, 2025

Dual-mode Imaging of Cutaneous Tissue Oxygenation and Vascular Function
11:35

Dual-mode Imaging of Cutaneous Tissue Oxygenation and Vascular Function

Published on: December 8, 2010

16.7K

Improving virtual try on clothes using image depth estimation.

Haniyeh Mobinizadeh1, Amir Lakizadeh2

  • 1Computer Engineering Department, University of Qom, Qom, Iran.

Scientific Reports
|September 1, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an enhanced virtual try-on framework using depth maps and attention mechanisms to improve garment alignment and realism. The new model effectively addresses occlusion challenges, delivering superior visual quality for virtual try-on applications.

Keywords:
Deep learningE-CommerceGenerative adversarial networksImage synthesisVirtual Try-On

More Related Videos

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

155
Author Spotlight: Advancements in 3D Optical Imaging for Comprehensive Body Composition Assessment in Modern Research
06:48

Author Spotlight: Advancements in 3D Optical Imaging for Comprehensive Body Composition Assessment in Modern Research

Published on: June 7, 2024

1.4K

Related Experiment Videos

Last Updated: Sep 9, 2025

Dual-mode Imaging of Cutaneous Tissue Oxygenation and Vascular Function
11:35

Dual-mode Imaging of Cutaneous Tissue Oxygenation and Vascular Function

Published on: December 8, 2010

16.7K
Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

155
Author Spotlight: Advancements in 3D Optical Imaging for Comprehensive Body Composition Assessment in Modern Research
06:48

Author Spotlight: Advancements in 3D Optical Imaging for Comprehensive Body Composition Assessment in Modern Research

Published on: June 7, 2024

1.4K

Area of Science:

  • Computer Vision
  • Computer Graphics
  • Artificial Intelligence

Background:

  • Image-based virtual try-on synthesizes clothing and person images for realistic visualizations.
  • Traditional methods suffer from misalignments and artifacts due to separate processing stages, especially with occlusions and complex poses.
  • Existing limitations reduce the realism and quality of virtual try-on outputs.

Purpose of the Study:

  • To develop an enhanced virtual try-on framework that overcomes limitations of traditional methods.
  • To improve garment alignment, reduce visual artifacts, and enhance the realism of generated try-on images.
  • To address challenges posed by occlusions and complex human poses in virtual try-on.

Main Methods:

  • Incorporation of depth maps for enhanced spatial awareness and precise garment alignment.
  • A refined garment-masking module for improved segmentation consistency and accurate garment representation.
  • Integration of multi-head attention mechanisms in feature extraction to preserve garment textures and details.

Main Results:

  • The proposed framework demonstrated significant enhancement in visual quality on a high-resolution dataset.
  • Effective mitigation of alignment and occlusion challenges, leading to more realistic virtual try-on results.
  • Outperformed baseline methods in delivering visually appealing and accurate virtual try-on images.

Conclusions:

  • The enhanced virtual try-on framework successfully addresses key challenges in garment alignment and occlusion.
  • The integration of depth maps and attention mechanisms leads to superior realism and quality in virtual try-on applications.
  • The proposed model offers a significant advancement for realistic virtual try-on experiences.