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GAN-based image-to-friction generation for tactile simulation of fabric material

Computers & graphics, v.102, 2022년, pp.460 - 473  

Cai, Shaoyu (School of Creative Media, City University of Hong Kong) ,  Zhao, Lu (Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology) ,  Ban, Yuki (Graduate School of Frontier Sciences, The University of Tokyo) ,  Narumi, Takuji (Graduate School of Information Science and Technology, The University of Tokyo) ,  Liu, Yue (Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology) ,  Zhu, Kening (School of Creative Media, City University of Hong Kong)

Abstract AI-Helper 아이콘AI-Helper

Abstract The electrovibration tactile display could render the tactile feeling of different textured surfaces by generating the frictional force through voltage modulation. When a user is sliding his/her finger on the display surface, he/she can feel the frictional texture. However, it is not trivi...

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참고문헌 (71)

  1. 10.1145/1866029.1866074 Bau O, Poupyrev I, Israr A, Harrison C. TeslaTouch: Electrovibration for touch surfaces. In: Proceedings of the 23nd annual ACM symposium on user interface software and technology. 2010. p. 283-92. 

  2. Acta Polytechnica Hungarica Galambos 9 1 41 2012 Vibrotactile feedback for haptics and telemanipulation: survey, concept and experiment 

  3. ACM Trans Appl Percept Bochereau 15 2 1 2018 10.1145/3152764 Perceptual constancy in the reproduction of virtual tactile textures with surface displays 

  4. Int J Hum-Comput Stud Zhu 130 234 2019 10.1016/j.ijhcs.2019.07.003 A sense of ice and fire: Exploring thermal feedback with multiple thermoelectric-cooling elements on a smart ring 

  5. 10.1145/3373625.3417004 Nasser A, Keng K-N, Zhu K. ThermalCane: Exploring thermotactile directional cues on cane-grip for non-visual navigation. In: Proceedings of the 22nd international ACM SIGACCESS conference on computers and accessibility. 2020. p. 1-12. 

  6. 10.1145/3281505.3281516 Chen T, Wu Y-S, Zhu K. Investigating different modalities of directional cues for multi-task visual-searching scenario in virtual reality. In: Proceedings of the 24th ACM symposium on virtual reality software and technology. 2018. p. 1-5. 

  7. 10.1145/3290605.3300923 Zhu K, Chen T, Han F, Wu Y-S. HapTwist: Creating interactive haptic proxies in virtual reality using low-cost twistable artefacts. In: Proceedings of the 2019 CHI conference on human factors in computing systems. 2019. p. 1-13. 

  8. 10.1145/3355049.3360529 Cai S, Ke P, Jiang S, Narumi T, Zhu K. Demonstration of thermairglove: A pneumatic glove for material perception in virtual reality through thermal and force feedback. In: SIGGRAPH Asia 2019 emerging technologies. 2019. p. 11-2. 

  9. Cai 2020 2020 IEEE conference on virtual reality and 3D user interfaces ThermAirGlove: A pneumatic glove for thermal perception and material identification in virtual reality 

  10. Shin 131 2015 2015 IEEE world haptics conference Data-driven modeling of isotropic haptic textures using frequency-decomposed neural networks 

  11. 10.1145/1978942.1979235 Israr A, Poupyrev I. Tactile brush: Drawing on skin with a tactile grid display. In: Proceedings of the SIGCHI conference on human factors in computing systems. 2011. p. 2019-28. 

  12. Text Res J Bueno 84 13 1428 2014 10.1177/0040517514521116 A simulation from a tactile device to render the touch of textile fabrics: A preliminary study on velvet 

  13. 10.1109/VR46266.2020.00043 Zhao L, Liu Y, Ye D, Ma Z, Song W. Implementation and evaluation of touch-based interaction using electrovibration haptic feedback in virtual environments. In: 2020 IEEE conference on virtual reality and 3D user interfaces. 2020. p. 239-47. 

  14. 10.1145/2984511.2984526 Benko H, Holz C, Sinclair M, Ofek E. Normaltouch and texturetouch: High-fidelity 3D haptic shape rendering on handheld virtual reality controllers. In: Proceedings of the 29th annual symposium on user interface software and technology. 2016. p. 717-28. 

  15. 10.1145/3173574.3173660 Whitmire E, Benko H, Holz C, Ofek E, Sinclair M. Haptic revolver: Touch, shear, texture, and shape rendering on a reconfigurable virtual reality controller. In: Proceedings of the 2018 CHI conference on human factors in computing systems. 2018. p. 1-12. 

  16. 10.1145/3173574.3174228 Choi I, Ofek E, Benko H, Sinclair M, Holz C. Claw: A multifunctional handheld haptic controller for grasping, touching, and triggering in virtual reality. In: Proceedings of the 2018 CHI conference on human factors in computing systems. 2018. p. 1-13. 

  17. 10.1145/3290605.3300479 Degraen D, Zenner A, Krüger A. Enhancing texture perception in virtual reality using 3D-printed hair structures. In: Proceedings of the 2019 CHI conference on human factors in computing systems. 2019. p. 1-12. 

  18. 10.1145/3290605.3300682 Sun Y, Yoshida S, Narumi T, Hirose M. Pacapa: A handheld VR device for rendering size, shape, and stiffness of virtual objects in tool-based interactions. In: Proceedings of the 2019 CHI conference on human factors in computing systems. 2019. p. 1-12. 

  19. Jiao 169 2018 2018 IEEE haptics symposium Data-driven rendering of fabric textures on electrostatic tactile displays 

  20. Int J Hum-Comput Interact Ilkhani 33 9 756 2017 10.1080/10447318.2017.1286766 Data-driven texture rendering on an electrostatic tactile display 

  21. Adv Neural Inf Process Syst Goodfellow 2672 2014 Generative adversarial nets 

  22. Jiao 331 2019 2019 IEEE world haptics conference Haptex: A database of fabric textures for surface tactile display 

  23. 10.1109/CVPR.2017.632 Isola P, Zhu J-Y, Zhou T, Efros AA. Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. p. 1125-34. 

  24. Cai 11 2020 International conference on artificial reality and telexistence & eurographics symposium on virtual environments FrictGAN: Frictional signal generation from fabric texture images using generative adversarial network 

  25. J Mach Learn Res Van der Maaten 9 11 2008 Visualizing data using t-SNE 

  26. Meyer 43 2013 2013 world haptics conference Fingertip friction modulation due to electrostatic attraction 

  27. 10.1145/1979742.1979705 Xu C, Israr A, Poupyrev I, Bau O, Harrison C. Tactile display for the visually impaired using TeslaTouch. In: Extended abstracts on human factors in computing systems. 2011. p. 317-22. 

  28. 10.1145/2501988.2502020 Kim S-C, Israr A, Poupyrev I. Tactile rendering of 3D features on touch surfaces. In: Proceedings of the 26th annual ACM symposium on user interface software and technology. 2013. p. 531-8. 

  29. IEEE Trans Haptics Romano 5 2 109 2012 10.1109/TOH.2011.38 Creating realistic virtual textures from contact acceleration data 

  30. Ilkhani 496 2014 International conference on human haptic sensing and touch enabled computer applications Data-driven texture rendering with electrostatic attraction 

  31. Osgouei 270 2018 2018 IEEE haptics symposium An inverse neural network model for data-driven texture rendering on electrovibration display 

  32. IEEE Trans Haptics Osgouei 13 2 298 2020 10.1109/TOH.2019.2932990 Data-driven texture modeling and rendering on electrovibration display 

  33. 10.1145/3290607.3312778 Zhao L, Liu Y, Ma Z, Wang Y. Design and evaluation of a texture rendering method for electrostatic tactile display. In: Extended abstracts of the 2019 CHI conference on human factors in computing systems. 2019. p. 1-6. 

  34. Culbertson 319 2014 2014 IEEE haptics symposium One hundred data-driven haptic texture models and open-source methods for rendering on 3D objects 

  35. Ujitoko 25 2018 International conference on human haptic sensing and touch enabled computer applications Vibrotactile signal generation from texture images or attributes using generative adversarial network 

  36. IEEE Trans Autom Sci Eng Liu 2020 Toward image-to-tactile cross-modal perception for visually impaired people 

  37. IEEE Robot Autom Lett Cai 6 4 7525 2021 10.1109/LRA.2021.3095925 Visual-tactile cross-modal data generation using residue-fusion GAN with feature-matching and perceptual losses 

  38. Wang 775 2014 2014 international conference on audio, language and image processing Electrostatic tactile rendering of image based on shape from shading 

  39. Vis Comput Wu 33 5 637 2017 10.1007/s00371-016-1214-3 Tactile modeling and rendering image-textures based on electrovibration 

  40. 10.1109/ICCV.2017.244 Zhu J-Y, Park T, Isola P, Efros AA. Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision. 2017. p. 2223-32. 

  41. Reed 2016 Generative adversarial text to image synthesis 

  42. 10.1109/ICCV.2017.629 Zhang H, Xu T, Li H, Zhang S, Wang X, Huang X et al. Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. In: Proceedings of the IEEE international conference on computer vision. 2017. p. 5907-15. 

  43. 10.1145/3126686.3126723 Chen L, Srivastava S, Duan Z, Xu C. Deep cross-modal audio-visual generation. In: proceedings of the on thematic workshops of ACM multimedia 2017. 2017. p. 349-57. 

  44. Hao 32 2018 Cmcgan: A uniform framework for cross-modal visual-audio mutual generation 

  45. Cogn Comput Syst Li 1 2 40 2019 10.1049/ccs.2018.0014 Learning cross-modal visual-tactile representation using ensembled generative adversarial networks 

  46. IEEE Trans Haptics Strese 10 2 226 2016 10.1109/TOH.2016.2625787 Multimodal feature-based surface material classification 

  47. 10.1109/CVPR.2017.19 Ledig C, Theis L, Huszár F, Caballero J, Cunningham A, Acosta A et al. Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. p. 4681-90. 

  48. ACM Trans. Graph. Chen 39 4 72:1 2020 10.1145/3386569.3392386 DeepFaceDrawing: Deep generation of face images from sketches 

  49. Aytar 2016 Soundnet: Learning sound representations from unlabeled video 

  50. Vondrick 2016 Generating videos with scene dynamics 

  51. Mirza 2014 Conditional generative adversarial nets 

  52. IEEE Trans Acoust Speech Signal Process Griffin 32 2 236 1984 10.1109/TASSP.1984.1164317 Signal estimation from modified short-time Fourier transform 

  53. Ronneberger 234 2015 International conference on medical image computing and computer-assisted intervention U-net: Convolutional networks for biomedical image segmentation 

  54. Arjovsky 2017 Towards principled methods for training generative adversarial networks 

  55. Arjovsky 2017 Wasserstein GAN 

  56. IEEE Trans Haptics Vardar 10 4 488 2017 10.1109/TOH.2017.2704603 Effect of waveform on tactile perception by electrovibration displayed on touch screens 

  57. Adv Neural Inf Process Syst Yoon 32 2019 Time-series generative adversarial networks 

  58. Heravi 11088 2020 2020 IEEE international conference on robotics and automation Learning an action-conditional model for haptic texture generation 

  59. McFee 2015 10.25080/Majora-7b98e3ed-003 Librosa: Audio and music signal analysis in python 

  60. J Mach Learn Res Pedregosa 12 2825 2011 Scikit-learn: Machine learning in Python 

  61. Glorot 249 2010 Proceedings of the thirteenth international conference on artificial intelligence and statistics Understanding the difficulty of training deep feedforward neural networks 

  62. Jin 2017 Towards the automatic anime characters creation with generative adversarial networks 

  63. Adv Neural Inf Process Syst Yoon 32 5508 2019 Time-series generative adversarial networks 

  64. Zhang 2017 Deep unsupervised clustering using mixture of autoencoders 

  65. Zophoniasson 70 2017 2017 zooming innovation in consumer electronics international conference Electrovibration: Influence of the applied force on tactile perception thresholds 

  66. J Big Data Sampath 8 1 1 2021 10.1186/s40537-021-00414-0 A survey on generative adversarial networks for imbalance problems in computer vision tasks 

  67. Neural Netw Buda 106 249 2018 10.1016/j.neunet.2018.07.011 A systematic study of the class imbalance problem in convolutional neural networks 

  68. Adv Robot Asano 28 16 1079 2014 10.1080/01691864.2014.913502 Toward quality texture display: Vibrotactile stimuli to modify material roughness sensations 

  69. Kingma 2013 Auto-encoding variational Bayes 

  70. Behav Brain Res Libouton 208 2 473 2010 10.1016/j.bbr.2009.12.017 Tactile roughness discrimination threshold is unrelated to tactile spatial acuity 

  71. Int J Neurosci Ardila 36 1-2 17 1987 10.3109/00207458709002135 Handedness and psychophysics: Weight and roughness 

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