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姓名 彭信穎(Hsin-Ying Peng) 查詢紙本館藏 畢業系所 資訊工程學系在職專班 論文名稱 影片指定對象臉部置換系統
(Face Replacement System For Designated Subjects Of The Video)相關論文 檔案 [Endnote RIS 格式]
[相關文章]
[文章引用]
[完整記錄]
[館藏目錄]
至系統瀏覽論文 ( 永不開放)
摘要(中) 目前臉部交換的應用大多都是要使用好幾萬張的使用者人臉照片以及被
交換的人臉照片，並且需要有一些程式基礎相關，且閱讀大量的文件說明，來
去做人臉交換應用的使用，過程中還會遇到各種環境問題，以及未知的錯誤，
本研究提出以網頁前後端加上 AWS 雲服務，並搭配人臉交換的應用程式，此架
構使用 Generator 與 Discriminator，並且主要目的是能夠用少量張數，來讓神經
網路學習圖像的風格到內容圖像的物體上，此程式只需要運行於後端 server，
在後端 server 將會搭配 nginx 以達負載均衡，使用者僅需提供一張使用者照片，
以及想要替換的目標的影片，並且能夠選擇目標影片中想要被交換的男主角或
女主角，透過網頁前端上傳後，再選擇欲被交換的對象，後端接收使用者的訊
息後，待後端處理完成，即可以取得最後成功換臉的影片。摘要(英) Most of the current face exchange applications use tens of thousands of user face photos and the exchanged face photos, and need to have some program basics knowledge, and read a lot of document descriptions to make face exchange applications. In the process of use, various environmental problems and unknown errors will be encountered. This research proposes to add AWS cloud services to the front and back ends of the web page, and to match the application of face exchange. This architecture uses Generator and Discriminator, and the main purpose is a small number of images can be used to allow the neural network to learn the style of the image on the object of the content image. This program only needs to run on the back- end server. The back-end server will be matched with nginx to achieve load balancing. The user only needs to provide a user photo, and the video of the target you want to replace, and you can select the male or female protagonist in the target film that you want to be exchanged. After uploading it through the front-end of the web page, you can select the object to be exchanged. Back-end After receiving the user′s message, after the back-end processing is completed, the final video of the successful face change can be obtained. 關鍵字(中) ★ 生成對抗式網路
★ 臉部置換關鍵字(英) ★ Generative Adversarial Network
★ Face Swap論文目次 目錄
第一章 緒論............................................................................................................................................1 1.1 研究動機.......................................................................................................................................1 1.2 研究目的.......................................................................................................................................1 1.3 論文架構.......................................................................................................................................2
第二章 技術回顧....................................................................................................................................2 2.1 CNN(Convolutional neural network[11]).....................................................................................2 2.2 ResNet50 特徵提取器..................................................................................................................4 2.3 Face Detection ..............................................................................................................................5 2.4 Generative Adversarial Network ..................................................................................................7
2.4.1 Auto-Encoder :.......................................................................................................................7
2.4.2 Generative Adversarial Network : .........................................................................................8 2.5 遷移式生成模型...........................................................................................................................9 2.5.1 生成器的設計......................................................................................................................10 2.5.2 判別器的設計......................................................................................................................11 2.5.3 損失函數的設計..................................................................................................................11 第三章 人臉影片置換應用..................................................................................................................12 3.1 系統架構.....................................................................................................................................12 3.2 網頁前後端.................................................................................................................................13 3.2.1 前端 HTML .........................................................................................................................13 3.2.2 後端 Flask............................................................................................................................15 3.2.3 Server in AWS EC2.............................................................................................................16 3.2.4 HTML 與 Flask 運作流程...................................................................................................18 3.2.5 Nginx....................................................................................................................................21 3.3 Face Detection ............................................................................................................................23 3.4 Face swap....................................................................................................................................24 3.5 新增指定臉部置換對象的功能.................................................................................................29 3.5.1 使用者介面流程..................................................................................................................29 3.5.1 指定置換的邏輯流程..........................................................................................................30 第四章 實驗結果..................................................................................................................................34 4.1 實驗環境.....................................................................................................................................34 4.2 資料集 ........................................................................................................................................35 4.3 驗證指標.....................................................................................................................................35
4.3.1 Inception Score ....................................................................................................................35 4.4 驗證結果.....................................................................................................................................36 4.5 臉部置換結果.............................................................................................................................36 4.6 指定影片角色置換結果.............................................................................................................41
第五章 結論與未來研究方向..............................................................................................................44參考文獻 [1] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-
Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial
nets. In Proc. NIPS, pages 2672–2680, 2014.
[2] Ming-Yu Liu1 , Xun Huang1,2 , Arun Mallya1 , Tero Karras1 , Timo Aila1 ,
Jaakko Lehtinen1,3 , Jan Kautz1 1NVIDIA, 2Cornell University, 3Aalto University.
Few-Shot Unsupervised Image-to-Image Translation
[3] Shifeng Zhang Xiangyu Zhu Zhen Lei* Hailin Shi Xiaobo Wang Stan Z. Li CBSR
& NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China
University of Chinese Academy of Sciences, Beijing, China. S3FD: Single Shot
Scale-invariant Face Detector
[4] Wei Liu1, Dragomir Anguelov2, Dumitru Erhan3, Christian Szegedy3, Scott
Reed4, Cheng-Yang Fu1, Alexander C. Berg11 UNC Chapel Hill 2Zoox Inc. 3Google
Inc. 4University of Michigan, Ann-Arbor. SSD: Single Shot MultiBox Detector
[5] Ming-Yu Liu1 , Xun Huang1,2 , Arun Mallya1 , Tero Karras1 , Timo Aila1 ,
Jaakko Lehtinen1,3 , Jan Kautz1 1NVIDIA, 2Cornell University, 3Aalto
University.Few-Shot Unsupervised Image-to-Image Translation
[6] https://brandinho.github.io/mario-ppo/
[7] https://aigeekprogrammer.com/convolutional-neural-network-image-recognition-
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[8] Diederik P. Kingma and Max Welling. Auto-encoding variational bayes. CoRR,
abs/1312.6114, 2013.
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[10] Zhirui Zhang, Shujie Liu, Mu Li, Ming Zhou, Enhong Chen University of
Science and Technology of China, Hefei, China Microsoft Research Asia.
Bidirectional Generative Adversarial Networks for Neural Machine Translation
[11] LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. Gradient-based learning
applied to document recognition. In Proc. IEEE, 1998.
[12] https://blog.keras.io/building-autoencoders-in-keras.html
[14] https://www.happycoding.today/posts/25
[16] https://hackmd.io/@s02260441/r1nDLeP2I
[13] https://github.com/jonbruner/generative-adversarial-networks/blob/master/gan-
notebook.ipynb
[15]
https://hackmd.io/@shaoeChen/HJiZtEngG/https%3A%2F%2Fhackmd.io%2Fs%2FH
yY5Rq5kG
[17] Rasmus Rothe1, Matthieu Guillaumin1, and Luc Van Gool1,2
1Computer Vision Laboratory, ETH Zurich, Switzerland.Non-Maximum Suppression
for Object Detection by Passing Messages between Windows.
[18] National Key Laboratory of Science and Technology on Multispectral
Information Processing,School of Automation,Huazhong University of Science &
Technology,China. BiSeNet: Bilateral Segmentation Network for Real-time Semantic
Segmentation
[19] https://arxiv.org/pdf/1801.01973.pdf
[22] Shane Barratt Rishi Sharma A Note on the Inception Score. arXiv:1801.01973
[20] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. Deep Residual Learning
for Image Recognition. arXiv:1512.03385 [cs.CV] 10 Dec 2015
[21] Xun Huang, Serge Belongie. Arbitrary Style Transfer in Real-time with Adaptive
Instance Normalization. arXiv:1703.06868 [cs.CV] In Proc. 2017
[22] Shane Barratt Rishi Sharma A Note on the Inception Score. arXiv:1801.01973指導教授 鄭旭詠(Hsu-Yung Cheng) 審核日期 2021-7-27 推文 plurk
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