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Depth Perception and Spatial Vision01:15

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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.
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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
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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

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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.