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題名:人工智慧藝術風格轉換之感知研究
作者:呂燕茹
作者(外文):LYU, YAN-RU
校院名稱:國立臺灣藝術大學
系所名稱:創意產業設計研究所
指導教授:林榮泰
林伯賢
學位類別:博士
出版日期:2022
主題關鍵詞:人工智慧類神經網路藝術風格轉換藝術感知Artificial IntelligentNeural NetworkArtistic style TransferArtistic Perception
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近年隨著人工智慧(artificial intelligence, AI)所引領的類神經網路深度學習技術日趨成熟,而其相關應用已經逐漸蔓延至包括藝術在內的各個領域,對藝術領域的創作、體驗、審美和欣賞將帶來新的機遇和挑戰。目前該領域多注重演算法的精進,而藝術審美太過複雜,討論AI介入藝術領域的問題主要涉及三個面向:(1)理性的科技層面,如何評價AI介入藝術的生成效果?(2)感性的審美層面,影響審美感受的關鍵因素為何?(3)藝術的創作層面,人類藝術家與AI技術之間存在何種關係?因此,本研究以類神經網路藝術風格轉換為例,比較經由AI技術轉換的藝術風格之感知差異,並探討影響感知的關鍵因素,進而構建適用於AI介入藝術創作的研究模式。
本研究通過執行三階段研究,依序討論繪畫藝術風格之感知要素、人機對藝術風格之感知差異、一般觀眾對藝術風格之感知差異。研究一邀請9位藝術背景專業篩選野獸派、表現主義、立體派、文藝復興時期的肖像畫作為風格圖,以京劇人物照片為內容圖,對經由類神經網路藝術風格轉換演算法生成的樣本進行感知比較,討論由編碼屬性(色彩、筆觸、紋理)和解碼層次(技術層、語意層、效果層)所構成評量矩陣從人性化感知角度評價轉換效果之可行性。邀請31位藝術背景專家參與實驗的結果顯示,不同的藝術風格經由人工智慧風格轉換後依然能夠被辨別,藝術家創作編碼過程中的色彩、筆觸、紋理等因素,足以影響觀眾對轉換結果的感知,其中「筆觸」和「紋理」扮演風格感知的關鍵屬性。另外在語義層和效果層屬性特徵的準確傳遞,能夠獲得更高的喜好度。為了進一步比較人機之間對藝術風格的感知差異,研究二依序執行問卷調查和眼動實驗。首先,邀請3位藝術家對研究一中的AI典型樣本進行優化,再由AI優化藝術家樣本,結合問卷調查和眼動實驗比較30餘位藝術背景專家感知三種不同操作結果之差異。結果表明,藝術家具有自調節的審美決策能力,在人機協作中佔據高層特徵的表達優勢,更易喚醒視覺。相反,演算法擅長低階視覺特徵的表徵,且對藝術的評判存在源於演算邏輯之偏見。研究三的普測實驗邀請232位一般觀眾對AI樣本和AI優化藝術家樣本的感知普測,檢驗AI生成藝術介面之溝通效果。結果顯示,專業、性別、學歷等因素會顯著影響受試者對藝術風格的感知,並且驗證了從低階到高階特徵的優化能夠顯著提升藝術風格的感知效果和喜好度。
經由研究可知,AI介入藝術創作的研究可以從以下面向入手:從藝術解碼的感知層次評價科技的介入、語義和情感傳遞影響觀賞者的感知、 藝術家與AI技術以彼此適應的耦合關係進行協作。最後,本研究提出一個「形式/造型/科技」與「儀式/思維/人性」的AI技術介入藝術創作的研究模式,將人工智慧的應用與評量置於系統架構中進行思考,用人類智慧評估人工智慧,以精進科技的人性化的效能,最終回饋人類世界。
In recent years, AI is becoming more mature with the development of neural network technology and access to various fields, including art creation. Meanwhile, it brings new opportunities and challenges in invention, experience, aesthetics, and appreciation of art. At present, this field only focuses on optimizing algorithms, but there are three problems in the improvement of algorithms. (1) As far as rational technology is concerned, how do we evaluate the results generated by the algorithm of artistic style transfer? (2) In terms of subjective aesthetic perception, what are the main factors that affect the artistic style transfer through computers? (3) For the creation of art, what is the relationship between artists and AI? By taking algorithms of artistic style transfer as an example, this study discusses the factors affecting the perception of painting style by comparing the differences between artists, AI, and audiences. Moreover, a research model for exploring the application of AI in the art world was proposed.
The three-stage study was carried out in sequence. The first-stage study evaluated the perception of art background experts on the style transfer of Fauvist, Expressionism, Cubism, and Renaissance portraits by the algorithm of artistic style transfer. Nine art background professionals were invited to select the style image, and the content image is a photo of Peking opera characters. This stage ensured the feasibility of the matrix constructed by style attributes (color, stroke, and texture) and perceptive levels (technical, semantic, and effectiveness). The results involved 31 art background experts as subjects and showed that different artistic styles can still be identified after transfer through AI. The factors such as color, stroke, and texture in the artist's coding process are enough to affect the audience's perception. Among them, stroke and texture play a more critical role. On the other hand, samples with a better perception of the semantic and effectiveness levels will get more preferences. Then, to explore the difference between humans and AI in the perception of artistic style, the second-stage experiments were carried out in order with questionnaires and eye-tracking. At first, three artists were invited to optimize the typical samples in the first-stage experiment and iterated these optimization through AI. Based on the data from more than 30 art background experts, it is shown that artists can self-adjust aesthetic decision-making with the advantage of artistic expression on the high-level features, which are easier to awaken human vision. On the contrary, the algorithms are good at representing low-order visual features. Besides, the algorithms are featured with deviation due to their mode of methods. In the third experiment, the feedback from 232 subjects with different backgrounds was collected to analyze their perception in AI samples and AI+artist samples to evaluate the communication degree of the application of AI in the field of Art. The major, gender and education background aspects can significantly affect their perception. In addition, it proved that the optimization from low-level to high-level features could improve the overall fitness and preference of the audience for artistic style transfer.The perceived effect of subjects' higher education and professional style will be significantly improved, and the perceived effect of subjects' higher education and professional style will be significantly improved.
Based on the results of the above three-stage experiments, the application of AI in the art can be studied from the following perspectives: the evaluation of the AI technology from the three perceptual levels; the influence of semantic and effectiveness levels on perception; the coupling partnership between artists and AI. Finally, a framework for the application and evaluation was proposed, which is from “form/ thinking/ technology” to “ceremony/ modeling/ humanity” by using human intelligence to improve AI technology and finally give back to the human world.
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