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Visual-to-Tactile Cross-Modal Generation Using a Class-Conditional GAN with Multi-Scale Discriminator and Hybrid

Nikolay Neshov1, Krasimir Tonchev1, Agata Manolova1

  • 1Faculty of Telecommunications, Technical University of Sofia, 8 Kliment Ohridski Blvd., 1000 Sofia, Bulgaria.

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Summary
This summary is machine-generated.

This study introduces a class-conditional Generative Adversarial Network (cGAN) to convert visual texture information into tactile spectrograms for realistic haptic feedback. The novel approach significantly improves cross-modal translation accuracy for virtual reality applications.

Keywords:
LMT-108 datasetaugmented realityconditional Generative Adversarial Network (cGAN)cross-modal generationhaptic feedbackhybrid lossmulti-scale discriminatortexture-to-tactile translationvibrotactile synthesisvirtual reality

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Area of Science:

  • Computer Vision
  • Haptics
  • Machine Learning

Background:

  • Visual texture perception is vital for haptic rendering and virtual reality.
  • Translating visual texture data into tactile feedback presents significant challenges.

Purpose of the Study:

  • To develop a class-conditional Generative Adversarial Network (cGAN) for cross-modal translation from texture images to vibrotactile spectrograms.
  • To enhance texture class semantics in the translation process for improved tactile realism.

Main Methods:

  • Utilized a pix2pix-adapted generator with Conditional Batch Normalization (CBN) and a DenseNet-201 label predictor.
  • Employed a multi-scale discriminator architecture (derived from pix2pixHD) for optimal perceptual similarity.
  • Implemented a hybrid loss function combining adversarial, L1, and feature matching losses.

Main Results:

  • Achieved superior perceptual similarity using Learned Perceptual Image Patch Similarity (LPIPS) and Fréchet Inception Distance (FID) metrics.
  • Demonstrated improved performance over existing models like pix2pix and pix2pixHD.
  • GradCAM visualizations confirmed the effectiveness of class conditioning.

Conclusions:

  • The proposed cGAN model effectively translates visual texture information into vibrotactile spectrograms.
  • Generated spectrograms can be converted to tactile signals, enabling advanced haptic feedback and virtual material simulation.
  • This work advances the field of cross-modal translation for immersive virtual experiences.