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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳炳宇(Bing-Yu Chen) | |
| dc.contributor.author | Li-Wen Su | en |
| dc.contributor.author | 蘇俐文 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:19:41Z | - |
| dc.date.available | 2021-10-04 | |
| dc.date.available | 2022-11-24T03:19:41Z | - |
| dc.date.copyright | 2021-10-04 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-09-30 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80866 | - |
| dc.description.abstract | 近期有許多利用對抗生成網路與形狀控制來達成圖像編輯的技術被發表，在形狀控制的例子中，3D Morphable Model (3DMM) 被拿來進行臉部圖像編輯的控制器。然而，目前並沒有一套允許使用者有系統的編輯人造物(例如：椅子、桌子、杯子等)的編輯方法。這其中主要理由是「在圖像上的編輯操作」與「三維操作」沒有一套很好的對應關係。 藉由利用圖像生成模型與三微形狀生成模型的優點，本文提出了一套流程讓使用者能直接於圖像上以物體部位為單位進行編輯，而「如何建立三維形狀特徵空間與圖像生成特徵空間之間的對應關係」即為此方法的重點。在本文提出的方法中，一張輸入圖像會先藉由一個前向對應函數連結到一個相應的三維形狀，使用者可以透過編輯該形狀來引導圖像做出相應的變化。本文展示了許多應用此方法的編輯作業：部位更換、部位大小調整以及視角操作，並進行大量且全面的實驗來評估此方法的有效性。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:19:41Z (GMT). No. of bitstreams: 1 U0001-2709202113452600.pdf: 11946344 bytes, checksum: 0dbba1e8d330559ff7cc1b47fd626263 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 口試委員會審定書 i 致謝 ii 中文摘要 iii Abstract iv List of Figures viii List of Tables xii Chapter 1 Introduction 1 Chapter 2 Related Work 5 2.1 Image-based shape reconstruction and editing 5 2.2 3D Shape Representation and Manipulation 6 2.3 GAN Inversion and Latent Space Manipulation 8 Chapter 3 Method 9 3.1 3D shape attribute parameterization 9 3.2 Cross-domain mapping framework 10 3.2.1 Image inversion 11 3.2.2 From W to S 11 3.2.3 From S to W 12 3.3 Training strategy and loss function 12 3.3.1 Data preparation 12 3.3.2 Training for MF 13 3.3.3 Training for MB 14 3.3.4 Finetuning for MF and MB 14 Chapter 4 Structural Image Editing 18 4.1 Part replacement 18 4.2 Part resizing 19 4.2.1 Resize trajectory 20 4.3 Viewing angle manipulation 21 Chapter 5 Experiment and Results 23 5.1 Implementation details 23 5.2 Image shape reconstruction 24 5.2.1 With/Without size finetuning for MF 24 5.2.2 Finetuning for MF and MB 24 5.3 Part replacement results 25 5.4 Part resizing results 26 5.5 Viewing angle manipulation results 30 5.6 Testing on real images 33 5.7 Overall discussion 33 Chapter 6 Limitation and Future Work 37 6.1 More editing 37 6.2 Fixed number of parts 38 6.3 Entanglement between parts 38 6.4 Complex shape trajectory for trajectory finetuner 38 6.5 Gap between synthesized images and real images 39 Chapter 7 Conclusion 41 Bibliography 42 | |
| dc.language.iso | en | |
| dc.subject | 互動設計 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 形狀表示 | zh_TW |
| dc.subject | 對抗生成網路 | zh_TW |
| dc.subject | Generative adversarial networks | en |
| dc.subject | Shape representations | en |
| dc.subject | Interaction design | en |
| dc.subject | Deep learning | en |
| dc.title | StylePart: 基於影像之物體形狀組件編輯 | zh_TW |
| dc.title | StylePart: Image-based Shape Part Manipulation | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 莊永裕(Hsin-Tsai Liu),王鈺強(Chih-Yang Tseng) | |
| dc.subject.keyword | 深度學習,對抗生成網路,形狀表示,互動設計, | zh_TW |
| dc.subject.keyword | Deep learning,Generative adversarial networks,Shape representations,Interaction design, | en |
| dc.relation.page | 48 | |
| dc.identifier.doi | 10.6342/NTU202103390 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-10-01 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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