Abstract
In this study, we have been developing a diminished reality system that allows users to experience becoming invisible human in order to reduce self-awareness and to improve their self-esteem. The system employs a camera to capture the user’s view and replaces his/her body images with background images in real time. These processed images are shown with a head mounted display to realize the immersive experience of becoming invisible human. The image inpainting is performed by a deep learning network. We also created a training and validation datasets and compared three networks. These networks are designed for image inpainting in this study. Moreover, we have made a hypothetical model of how psychological states and self-awareness will change when experiencing the developed system. In the future work, we are planning to conduct an experiment and confirm whether use of the system improves self-esteem. Also, we will investigate the process of changing the psychological state based on the hypothetical model by questionnaire surveys.
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References
Shirai, T.: Rethinking of educational practice targeting promotion of self-esteem. Memoirs Osaka Kyoiku Univ. 64(1), 147–156 (2015)
Mizuta, K., The effect of objective self awareness on self-evaluation. Japan. J. Exp. Social Psychol. 27(1), 59–67 (1987–1988)
Pathak, D., Krähenbühl, P., Donahue, J., Darrell, T., Efros, A.: Context encoders: feature learning by inpainting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2536–2544 (2016)
Yang, C., Lu, X., Lin, Z., Shechtman, E., Wang, O., Li, H., High-resolution image inpainting using multi-scale neural patch synthesis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6721–6729 (2017)
Seychell, D., Debono, C.J.: An approach for objective quality assessment of image inpainting results. In: 2020 IEEE 20th Mediterranean Electrotechnical Conference (MELECON), pp. 226–231 (2020)
Cai, N., Su, Z., Lin, Z., Wang, H., Yang, Z., Ling, B.W.-K.: Blind inpainting using the fully convolutional neural network. Visual Comput. 33(2), 249–261 (2015). https://doi.org/10.1007/s00371-015-1190-z
Salem, N.: A survey on various image inpainting techniques. Fut. Eng. J. 2(2), 1 (2021)
Glocker, B., Izadi, S., Shotton, J., Criminisi, A.: Real-time RGB-D camera relocalization. In: 2013 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 173–179 (2013)
Hayakawa, A., et al.: Neural network libraries: a deep learning framework designed from engineers’ perspectives (2021)
Martini, M., Kilteni, K., Maselli, A., Sanchez-Vives, M.V.: The body fades away: investigating the effects of transparency of an embodied virtual body on pain threshold and body ownership. Sci. Rep. 5(13948), 1–8 (2015)
Mizuma, R.: A study of relationship between an ideal self and self-esteem, and consciousness to self-formation. Japan. J. Educ. Psychol. 46(2), 131–141 (1998)
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Sasaki, M., Ishii, H., Ueda, K., Shimoda, H. (2022). Development of an Invisible Human Experience System Using Diminished Reality. In: Chen, J.Y.C., Fragomeni, G. (eds) Virtual, Augmented and Mixed Reality: Design and Development. HCII 2022. Lecture Notes in Computer Science, vol 13317. Springer, Cham. https://doi.org/10.1007/978-3-031-05939-1_33
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DOI: https://doi.org/10.1007/978-3-031-05939-1_33
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