學術產出-Theses
Article View/Open
Publication Export
-
題名 《幻意視界》- 以生成式藝術與眼動追蹤技術探討意識的變幻
《Illusory Eyescape》- Exploring the Variations of Consciousness through Generative Art and Eye-Tracking Techniques作者 李欣霏
Lee, Sin-Fei貢獻者 紀明德<br>陶亞倫
Chi, Ming-Te<br>Tao, Ya-Lun
李欣霏
Lee, Sin-Fei關鍵詞 生成式藝術
眼動追蹤
互動式藝術日期 2024 上傳時間 1-Mar-2024 14:13:35 (UTC+8) 摘要 在科技的洪流中，藝術形式與數位技術不斷交融、適應、演變，從機械複製時代到虛擬再現真實，到現在連生成作品的工作都變成一句咒語就能夠解決的事，那麼究竟藝術創作的本質到底是甚麼?瑞士藝術家阿爾伯托·賈科梅蒂（Alberto Giacometti）曾說：「藝術品不是再現真實，而是創造具有相同強度的真實。」在此，藝術作品僅作為表達真實感受的一種媒介，其本質在於超出物性所展現出無形的精神與情感。 因此本論文旨在利用科技藝術創作的方式來探討人與機器的意識差異，以及透過生成式人工智慧來呈現藝術作品的本質，同時，我們也將關注語言在藝術創作中的限制。在創作過程中經由文獻探討梳理想法，咀嚼吸收後成為創作的養分，最終進行作品展覽，與觀眾合力完成作品。 作品以眼動儀追蹤獲取觀眾意識的視覺資訊做為生成式人工智慧的輸入，並根據輸入產出畫作後顯現。利用各種視覺意象如：觀者的眼睛、眼動追蹤、致敬比利時藝術家雷內·馬格利特畫作「虛假的鏡子」等等，象徵人類的存在和自由以及討論主觀和客觀等意義。創作作品須透過觀眾親自體驗的過程來感受人類「意識」的重要性。 參考文獻 [1] E. Mansimov, E. Parisotto, J. L. Ba, and R. Salakhutdinov, “Generating images from captions with attention,” arXiv preprint arXiv:1511.02793, 2015. [2] P. Wolfendale, Object-oriented philosophy: The noumenon’s new clothes. MIT Press, 2019, vol. 1. [3] M. Coeckelbergh, “Can machines create art?” Philosophy & Technology, vol. 30, no. 3, pp. 285–303, 2017. [4] J.-W. Hong and N. M. Curran, “Artificial intelligence, artists, and art: attitudes toward artwork produced by humans vs. artificial intelligence,” ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), vol. 15, no. 2s, pp. 1–16, 2019. [5] E. S. Mikalonytė and M. Kneer, “Can artificial intelligence make art?: Folk intuitions as to whether ai-driven robots can be viewed as artists and produce art,” ACM Transactions on Human-Robot Interaction (THRI), vol. 11, no. 4, pp. 1–19, 2022. [6] A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever, “Zero-shot text-to-image generation,” pp. 8821–8831, 2021. [7] G. M. Edelman, Neural Darwinism: The theory of neuronal group selection. Basic books, 1987. [8] G. M. Edelman and G. Tononi, A universe of consciousness: How matter becomes imagination. Hachette UK, 2008. [9] 傑拉爾德·M·埃德爾曼、朱利歐·托諾尼, 意識的宇宙：物質如何轉變 為精神（重譯版）, 2019. [10] G. Tononi, “An information integration theory of consciousness,” BMC neuroscience, vol. 5, pp. 1–22, 2004. [11] A. Haun and G. Tononi, “Why does space feel the way it does? towards a principled account of spatial experience,” Entropy, vol. 21, no. 12, p. 1160, 2019. [12] B. J. Baars, A cognitive theory of consciousness. Cambridge University Press, 1993. [13] ——, “Global workspace theory of consciousness: toward a cognitive neuroscience of human experience,” Progress in brain research, vol. 150, pp. 45–53, 2005. [14] S. Dehaene, M. Kerszberg, and J.-P. Changeux, “A neuronal model of a global workspace in effortful cognitive tasks,” Proceedings of the national Academy of Sciences, vol. 95, no. 24, pp. 14 529–14 534, 1998. [15] R. VanRullen and R. Kanai, “Deep learning and the global workspace theory,” Trends in Neurosciences, vol. 44, no. 9, pp. 692–704, 2021. [16] N. Block, “How many concepts of consciousness?” Behavioral and brain sciences, vol. 18, no. 2, pp. 272–287, 1995. [17] K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, R. Zemel, and Y. Bengio, “Show, attend and tell: Neural image caption generation with visual attention,” pp. 2048–2057, 2015. [18] O. Vinyals, A. Toshev, S. Bengio, and D. Erhan, “Show and tell: A neural image caption generator,” pp. 3156–3164, 2015. [19] K. Gregor, I. Danihelka, A. Graves, D. Rezende, and D. Wierstra, “Draw: A recurrent neural network for image generation,” pp. 1462–1471, 2015. [20] A. Mordvintsev, C. Olah, and M. Tyka, “Inceptionism: Going deeper into neural networks,” 2015. [21] L. A. Gatys, A. S. Ecker, and M. Bethge, “A neural algorithm of artistic style,” arXiv preprint arXiv:1508.06576, 2015. [22] J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” pp. 2223–2232, 2017. [23] T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” pp. 4401–4410, 2019. [24] J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems, vol. 33, pp. 6840–6851, 2020. [25] A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al., “Learning transferable visual models from natural language supervision,” pp. 8748–8763, 2021. [26] R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” pp. 10 684–10 695, 2022. [27] L. Wittgenstein and R. Monk, Tractatus logico-philosophicus. Routledge, 2013. [28] M. O’Sullivan, An Analysis of Ludwig Wittgenstein’s Philosophical Investigations. Macat Library, 2017. [29] T. Nagel, “What is it like to be a bat?” pp. 159–168, 1980. [30] G. Morrot, F. Brochet, and D. Dubourdieu, “The color of odors,” Brain and language, vol. 79, no. 2, pp. 309–320, 2001. [31] G. Harman, Object-oriented ontology: A new theory of everything. Penguin UK, 2018. [32] E. Husserl, Cartesian meditations: An introduction to phenomenology. Springer Science & Business Media, 2013. [33] A. Papoutsaki, P. Sangkloy, J. Laskey, N. Daskalova, J. Huang, and J. Hays, “Webgazer: Scalable webcam eye tracking using user interactions,” in Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI). AAAI, 2016, pp. 3839–3845. [34] 大學入學考試中心研究發展處, “高中英文參考詞彙表,” https://www.ceec. edu.tw/SourceUse/ce37/ce37.htm. [35] K. Rayner, “Eye movements in reading and information processing: 20 years of research.” Psychological bulletin, vol. 124, no. 3, p. 372, 1998. [36] T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” arXiv preprint arXiv:1301.3781, 2013. [37] Q. Le and T. Mikolov, “Distributed representations of sentences and documents,” in International conference on machine learning. PMLR, 2014, pp. 1188–1196. 描述 碩士
國立政治大學
數位內容碩士學位學程
110462008資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110462008 資料類型 thesis dc.contributor.advisor 紀明德<br>陶亞倫 zh_TW dc.contributor.advisor Chi, Ming-Te<br>Tao, Ya-Lun en_US dc.contributor.author (Authors) 李欣霏 zh_TW dc.contributor.author (Authors) Lee, Sin-Fei en_US dc.creator (作者) 李欣霏 zh_TW dc.creator (作者) Lee, Sin-Fei en_US dc.date (日期) 2024 en_US dc.date.accessioned 1-Mar-2024 14:13:35 (UTC+8) - dc.date.available 1-Mar-2024 14:13:35 (UTC+8) - dc.date.issued (上傳時間) 1-Mar-2024 14:13:35 (UTC+8) - dc.identifier (Other Identifiers) G0110462008 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/150265 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 數位內容碩士學位學程 zh_TW dc.description (描述) 110462008 zh_TW dc.description.abstract (摘要) 在科技的洪流中，藝術形式與數位技術不斷交融、適應、演變，從機械複製時代到虛擬再現真實，到現在連生成作品的工作都變成一句咒語就能夠解決的事，那麼究竟藝術創作的本質到底是甚麼?瑞士藝術家阿爾伯托·賈科梅蒂（Alberto Giacometti）曾說：「藝術品不是再現真實，而是創造具有相同強度的真實。」在此，藝術作品僅作為表達真實感受的一種媒介，其本質在於超出物性所展現出無形的精神與情感。 因此本論文旨在利用科技藝術創作的方式來探討人與機器的意識差異，以及透過生成式人工智慧來呈現藝術作品的本質，同時，我們也將關注語言在藝術創作中的限制。在創作過程中經由文獻探討梳理想法，咀嚼吸收後成為創作的養分，最終進行作品展覽，與觀眾合力完成作品。 作品以眼動儀追蹤獲取觀眾意識的視覺資訊做為生成式人工智慧的輸入，並根據輸入產出畫作後顯現。利用各種視覺意象如：觀者的眼睛、眼動追蹤、致敬比利時藝術家雷內·馬格利特畫作「虛假的鏡子」等等，象徵人類的存在和自由以及討論主觀和客觀等意義。創作作品須透過觀眾親自體驗的過程來感受人類「意識」的重要性。 zh_TW dc.description.tableofcontents 第一章 緒論 1 1.1 研究動機與目的 1 1.2 研究方法與步驟 3 1.2.1 研究方法 3 1.2.2 研究步驟 5 1.3 研究範圍與限制 7 1.3.1 研究範圍 7 1.3.2 研究限制 7 第二章 文獻探討 10 2.1 觀念藝術 ( Conceptual Art ) 10 2.2 人類與機器的意識 11 2.2.1 意識（consciousness） 11 2.2.2 早期對意識之研究 11 2.2.3 當科學滲入意識 14 2.2.4 機器意識 15 2.3 文字語言與藝術 19 2.3.1 文生圖（Text to image） 19 2.3.2 語言的界線 22 2.3.3 語言作為使用的意義 24 2.4 相關作品 26 2.4.1 一把與三把椅子 26 2.4.2 一把與 N 把椅子 28 2.4.3 形象的叛逆 29 2.4.4 虛假的鏡子 30 第三章 創作理念與形式 32 3.1 創作理念 32 3.1.1 語言之於人與機（The Language） 32 3.1.2 人的意識（Human Consciousness） 33 3.1.3 視覺意象（Visual Imagery） 34 3.1.4 物共構（Object Co-Construct） 35 3.1.5 主體間性（Intersubjectivity） 38 3.2 創作形式 38 3.2.1 創作規劃 38 3.2.2 創作媒材 41 3.2.3 創作流程 43 第四章 創作歷程 46 4.1 創作內容 46 4.1.1 閱讀資料設計 46 4.1.2 閱讀版面設計 47 4.1.3 資料提取設計 49 4.1.4 文生圖作品設計 50 4.2 體驗流程 50 4.3 創作展覽 52 4.3.1 展覽主視覺 52 4.3.2 展覽空間 53 4.3.3 展覽紀實 54 4.4 創作回饋與觀察 57 4.4.1 預期效果 57 4.4.2 實際開發限制 57 4.4.3 觀察與反饋 58 4.5 小結 59 第五章 結論與建議 61 5.1 結論與反思 61 5.2 未來發展方向 62 參考文獻 65 zh_TW dc.format.extent 40439300 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110462008 en_US dc.subject (關鍵詞) 生成式藝術 zh_TW dc.subject (關鍵詞) 眼動追蹤 zh_TW dc.subject (關鍵詞) 互動式藝術 zh_TW dc.title (題名) 《幻意視界》- 以生成式藝術與眼動追蹤技術探討意識的變幻 zh_TW dc.title (題名) 《Illusory Eyescape》- Exploring the Variations of Consciousness through Generative Art and Eye-Tracking Techniques en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] E. Mansimov, E. Parisotto, J. L. Ba, and R. Salakhutdinov, “Generating images from captions with attention,” arXiv preprint arXiv:1511.02793, 2015. [2] P. Wolfendale, Object-oriented philosophy: The noumenon’s new clothes. MIT Press, 2019, vol. 1. [3] M. Coeckelbergh, “Can machines create art?” Philosophy & Technology, vol. 30, no. 3, pp. 285–303, 2017. [4] J.-W. Hong and N. M. Curran, “Artificial intelligence, artists, and art: attitudes toward artwork produced by humans vs. artificial intelligence,” ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), vol. 15, no. 2s, pp. 1–16, 2019. [5] E. S. Mikalonytė and M. Kneer, “Can artificial intelligence make art?: Folk intuitions as to whether ai-driven robots can be viewed as artists and produce art,” ACM Transactions on Human-Robot Interaction (THRI), vol. 11, no. 4, pp. 1–19, 2022. [6] A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever, “Zero-shot text-to-image generation,” pp. 8821–8831, 2021. [7] G. M. Edelman, Neural Darwinism: The theory of neuronal group selection. Basic books, 1987. [8] G. M. Edelman and G. Tononi, A universe of consciousness: How matter becomes imagination. Hachette UK, 2008. [9] 傑拉爾德·M·埃德爾曼、朱利歐·托諾尼, 意識的宇宙：物質如何轉變 為精神（重譯版）, 2019. [10] G. Tononi, “An information integration theory of consciousness,” BMC neuroscience, vol. 5, pp. 1–22, 2004. [11] A. Haun and G. Tononi, “Why does space feel the way it does? towards a principled account of spatial experience,” Entropy, vol. 21, no. 12, p. 1160, 2019. [12] B. J. Baars, A cognitive theory of consciousness. Cambridge University Press, 1993. [13] ——, “Global workspace theory of consciousness: toward a cognitive neuroscience of human experience,” Progress in brain research, vol. 150, pp. 45–53, 2005. [14] S. Dehaene, M. Kerszberg, and J.-P. Changeux, “A neuronal model of a global workspace in effortful cognitive tasks,” Proceedings of the national Academy of Sciences, vol. 95, no. 24, pp. 14 529–14 534, 1998. [15] R. VanRullen and R. Kanai, “Deep learning and the global workspace theory,” Trends in Neurosciences, vol. 44, no. 9, pp. 692–704, 2021. [16] N. Block, “How many concepts of consciousness?” Behavioral and brain sciences, vol. 18, no. 2, pp. 272–287, 1995. [17] K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, R. Zemel, and Y. Bengio, “Show, attend and tell: Neural image caption generation with visual attention,” pp. 2048–2057, 2015. [18] O. Vinyals, A. Toshev, S. Bengio, and D. Erhan, “Show and tell: A neural image caption generator,” pp. 3156–3164, 2015. [19] K. Gregor, I. Danihelka, A. Graves, D. Rezende, and D. Wierstra, “Draw: A recurrent neural network for image generation,” pp. 1462–1471, 2015. [20] A. Mordvintsev, C. Olah, and M. Tyka, “Inceptionism: Going deeper into neural networks,” 2015. [21] L. A. Gatys, A. S. Ecker, and M. Bethge, “A neural algorithm of artistic style,” arXiv preprint arXiv:1508.06576, 2015. [22] J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” pp. 2223–2232, 2017. [23] T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” pp. 4401–4410, 2019. [24] J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems, vol. 33, pp. 6840–6851, 2020. [25] A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al., “Learning transferable visual models from natural language supervision,” pp. 8748–8763, 2021. [26] R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” pp. 10 684–10 695, 2022. [27] L. Wittgenstein and R. Monk, Tractatus logico-philosophicus. Routledge, 2013. [28] M. O’Sullivan, An Analysis of Ludwig Wittgenstein’s Philosophical Investigations. Macat Library, 2017. [29] T. Nagel, “What is it like to be a bat?” pp. 159–168, 1980. [30] G. Morrot, F. Brochet, and D. Dubourdieu, “The color of odors,” Brain and language, vol. 79, no. 2, pp. 309–320, 2001. [31] G. Harman, Object-oriented ontology: A new theory of everything. Penguin UK, 2018. [32] E. Husserl, Cartesian meditations: An introduction to phenomenology. Springer Science & Business Media, 2013. [33] A. Papoutsaki, P. Sangkloy, J. Laskey, N. Daskalova, J. Huang, and J. Hays, “Webgazer: Scalable webcam eye tracking using user interactions,” in Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI). AAAI, 2016, pp. 3839–3845. [34] 大學入學考試中心研究發展處, “高中英文參考詞彙表,” https://www.ceec. edu.tw/SourceUse/ce37/ce37.htm. [35] K. Rayner, “Eye movements in reading and information processing: 20 years of research.” Psychological bulletin, vol. 124, no. 3, p. 372, 1998. [36] T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” arXiv preprint arXiv:1301.3781, 2013. [37] Q. Le and T. Mikolov, “Distributed representations of sentences and documents,” in International conference on machine learning. PMLR, 2014, pp. 1188–1196. zh_TW
