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Subjective Evaluation of the Predictive Performance and Operability of a Two-Layer Regression-Based Texture Mixing

Keisuke Tozuka, Genki Sasaki, Masamichi Sakaguchi

IEEE Transactions on Haptics

|June 22, 2026

Summary

This study introduces a texture mixing system for virtual environments, enhancing user-driven texture creation. The system achieves comparable reproduction performance to reference models, enabling users to accurately create target textures.

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Area of Science:

Computer Graphics
Human-Computer Interaction
Virtual Reality

Background:

Virtual environments require intuitive methods for texture creation.
Previous work established a two-layer regression model for texture synthesis.

Purpose of the Study:

To develop and evaluate an interactive texture mixing system for user-driven texture creation.
To improve the extrapolative performance of the texture model through dataset and training enhancements.

Main Methods:

Implemented a two-layer regression model with improved dataset and training strategies.
Developed an interactive system allowing users to adjust texture features via sliders.
Conducted objective spectral similarity and subjective similarity evaluations.

Related Experiment Videos

Main Results:

The proposed model's reproduction performance was comparable to a data-driven reference model for approximately 60% of textures.
Nonexpert users could accurately adjust sliders to approach target textures within one minute under simplified conditions.

Conclusions:

The developed texture mixing system effectively supports user-driven texture creation in virtual environments.
The system demonstrates good performance and usability for nonexpert users in approaching target textures.