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研究生:陳思頴
研究生(外文):Sih-Ying Chen
論文名稱:多尺度區域強化之姿態遷移用於自動人像生成
論文名稱(外文):Multi-Scale Region Reinforcement on Pose Transfer for Automatic Person Image Generation
指導教授:鄭旭詠
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:64
中文關鍵詞:生成對抗網路姿態轉換OpenPose
外文關鍵詞:Generative Adversarial NetworkPose TransferOpenPose
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随著人工智慧與深度學習領域的蓬勃發展,已被廣泛應用於不同領域中,
不論是在語義分析、影像識別等,都有相當顯著的貢獻。如今人工智慧的目標
不在是讓電腦擁有智慧,而是希望讓電腦也具有創造力,如寫詩、作曲、或者
是影像生成等,透過人工智慧,無中生有,創造出無限潛能。本篇論文提供一
個姿態遷移系統,藉由人物圖像與目標姿態,讓電腦自動生成出符合目標姿態
的人物圖像。
本論文使用了漸進式的姿態遷移生成模型架構,透過漸近式的方式將人物
圖像的姿態轉換至目標姿態。在轉換的過程中,我們提出了多尺度區域提取器
(Multi-Scale Region Extractor),透過擷取人物影像中特定的區域位置的特徵圖,
來改善自動編碼器遺失資料訊息的問題,同時也降低了姿態遷移中斷肢的可能
性。並針對於多尺度區域特徵提取器,設計了區域風格損失函數 (Region Style
Loss),來優化訓練生成模型的過程。最後,基於本系統的架構下,只要使用一
張人物圖像,便可以針對喜好生成出不同舞蹈風格的影片。
With the vigorous development of artificial intelligence and deep learning, they
have been widely used in different fields. Whether in semantic analysis, image
recognition, etc., there are quite significant contributions. The goals of artificial
intelligence are to make computer creative, such as writing poems, composing, or
making images, making out of noting, rather than to have intelligence. This thesis
proposes a DanceGAN, which can make computer generate character images that
matches the target posture automatically.
In this thesis, we use a progressive pose transfer to generate a model architecture,
which transforms the pose of the character images to the target pose in an asymptotic
manner. In the transform process, we propose the Multi-Scale Region Extractor to
capture specific area of the character image to improve the missing data message
problems of auto encoder. We also design the Region Style Loss for Multi-Scale
Region Extractor to improve the training process of generating model. Finally, based
on the architecture of this system, we can generate different dancing style according
to your favorite using only one character image.
摘要 V
Abstract VI
致謝 VII
目錄 VIII
圖目錄 X
表目錄 XI
第一章 緒論 1
1.1 研究動機 1
1.2 相關文獻 2
1.3 系統架構 5
1.4 論文架構 6
第二章 文獻回顧 7
2.1 DeepFashion資料集 7
2.2 VGG-19 網路模型的特徵提取器 8
2.3 圖像語義切割 9
2.4 生成網路模型 11
2.4.1 AutoEncoder 12
2.4.2 Generative Adversarial Network 13
第三章 研究方法與系統程式 16
3.1 資料集 17
3.2 遷移式的生成模型 18
3.2.1 Encoder & Decoder 19
3.2.1.1 Multiple Scale Region Extractor 19
3.2.1.2 Learnable Region Normalization 21
3.2.2 Pose-Attentional Transfer Network 25
3.3 鑑別器 27
3.4 損失函數 27
第四章 實驗結果 30
4.1 設備環境 30
4.2 資料集 30
4.3 驗證指標 31
4.3.1 Inception Score 31
4.3.2 SSIM(Structural Similarity) 32
4.4 方法比較 33
4.4.1 Pose-Transfer GAN v.s. DanceGAN 33
4.4.2 不同的區域提取用於MSRE 37
4.4.3 不同標準化的影響 44
4.5 速度評測 47
第五章 結論與未來研究方向 48
參考文獻 49
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