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Perceptual Constancy

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Related Experiment Video

Updated: Jan 17, 2026

Applying Incongruent Visual-Tactile Stimuli during Object Transfer with Vibro-Tactile Feedback

Applying Incongruent Visual-Tactile Stimuli during Object Transfer with Vibro-Tactile Feedback

Published on: May 23, 2019

Perceptually-Guided VR Style Transfer.

Seonghwa Choi, Jungwoo Huh, Sanghoon Lee

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|September 16, 2025

Summary

This summary is machine-generated.

This study introduces a new VR style transfer model (VRST-GAN) that enhances immersion by optimizing color dynamics and spatio-temporal consistency in virtual reality content. A VR Immersiveness Predictor (VR-IP) achieves 91% accuracy in predicting user immersion.

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Last Updated: Jan 17, 2026

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Area of Science:

Computer Vision
Human-Computer Interaction
Perceptual Computing

Background:

Virtual reality (VR) offers immersive experiences using omnidirectional videos (ODVs).
Enhancing viewer quality of experience (QoE) in VR requires manipulating scenes based on perceptual factors.
Style transfer methods can create artistic effects in VR, but their impact on immersion needs study.

Purpose of the Study:

To investigate perceptual factors influencing stylized immersiveness in VR.
To develop a perceptually-guided VR style transfer model.
To create a model for predicting immersiveness in VR content.

Main Methods:

Introduced an immersiveness sensitivity model for luminance and color perception.
Measured color dynamics and spatio-temporal consistency in stylized VR content.
Developed VR Style Transfer GAN (VRST-GAN) and VR Immersiveness Predictor (VR-IP).

Main Results:

VRST-GAN enhances VR immersiveness by considering color dynamics and spatio-temporal consistency.
VRST-GAN effectively transfers desired styles into VR environments.
VR Immersiveness Predictor (VR-IP) achieved 91% accuracy in predicting user immersion.

Conclusions:

Perceptual factors like color dynamics and spatio-temporal consistency are crucial for stylized VR immersiveness.
VRST-GAN offers a novel approach to creating more immersive VR content.
The VR-IP model demonstrates the feasibility of predicting VR immersion accurately.