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研究生:潘宗煒
研究生(外文):PAN, ZONG-WEI
論文名稱:共同考慮時間及多視角資訊的弱監督式人體三維姿態預估
論文名稱(外文):Weakly Supervised 3D Human Pose Estimation by Joint Considering Temporal and Multiview Information
指導教授:江振國朱威達
指導教授(外文):CHIANG, CHEN-KUOCHU, WEI-TA
口試委員:胡敏君黃敬群江振國朱威達
口試委員(外文):HU, MIN-CHUNHUANG, CHING-CHUNCHIANG, CHEN-KUOCHU, WEI-TA
口試日期:2020-07-29
學位類別:碩士
校院名稱:國立中正大學
系所名稱:資訊工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:英文
論文頁數:28
中文關鍵詞:三維人體姿態預估弱監督式學習時間資訊多視角資訊
外文關鍵詞:3D human pose estimationweakly supervisedtemporal informationmultiview information
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人體三維姿態常利用監督式學習的方法來預估,這種方法的缺點是需要大量的二維骨架、三維骨架配對資料。但收集三維骨架資料的成本相當昂貴且花時間,因此近年來許多研究開始使用弱監督式學習的方法在預估人體三維姿態,希望能只透過少量的3D data就能有效的訓練。
在弱監督式學習的方法中,有人提出分別考慮時間資訊或多視角資訊來訓練模型。在本論文中,我們提出一個基於對抗式生成網路的弱監督式學習方法,同時整合時間資訊跟多視角資訊,取得目前為止以弱監督式學習為基礎的方法中最好的人體姿態預估的結果。
Three-dimensional human pose estimation is usually conducted in a supervised manner. Developing a supervised model requires a large amount of 2D and 3D paired skeletons. Collecting labeled 3D skeletons, however, is quite expensive and time-consuming. Many studies thus have been proposed to estimate 3D human pose in a weakly supervised manner. In this way, only a small amount of 3D data is needed to train an effective model.
In weakly supervised learning methods, some studies have been independently proposed to consider time information or multiview information for training. In this thesis, we propose a weakly supervised learning method based on the generative adversarial network. We jointly consider time information and multiview information and achieve state-of-the-art results.
摘要 i
Abstract ii
List of Figures iii
List of Tables iv
Contents v
1 Introduction 1
1.1 Motivation 1
1.2 System Overview 3
1.3 Contributions 4
1.4 Thesis Organization 4
2 Related Works 6
2.1 Traditional Methods 6
2.2 Deep-based Methods 6
2.3 Short Summary 7
3 Proposed Methods 8
3.1 RepNet 8
3.2 Considering Temporal Information 9
3.3 Considering Multiview Information 11
3.4 Short Summary 12
4 Evaluation 13
4.1 Dataset and Experimental Settings 13
4.2 Performance 16
4.2.1 Comparing with Supervised Methods 16
4.2.2 Comparing with Weakly-Supervised Methods 17
5 Conclusion and Future Works 22
5.1 Conclusion 22
5.2 Future Works 23
References 24
Appendix 27
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