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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 簡韶逸 | zh_TW |
| dc.contributor.advisor | Shao-Yi Chien | en |
| dc.contributor.author | 范詠為 | zh_TW |
| dc.contributor.author | Yung-Wei Fan | en |
| dc.date.accessioned | 2025-11-26T16:27:47Z | - |
| dc.date.available | 2025-11-27 | - |
| dc.date.copyright | 2025-11-26 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-09-16 | - |
| dc.identifier.citation | H. Akada, J. Wang, S. Shimada, M. Takahashi, C. Theobalt, and V. Golyanik, “Unrealego: A new dataset for robust egocentric 3d human motion capture,” in European Conference on Computer Vision. Springer, 2022, pp. 1–17.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/101011 | - |
| dc.description.abstract | 自我視角三維人體姿態估計能夠在虛擬實境（VR）和擴增實境（AR）應用中提供自然且沉浸式的互動，無需依賴外部攝影機或手持控制器。這種方法為全身動作捕捉提供了一種靈活且便攜的解決方案，可無縫整合至頭戴式顯示裝置（HMD）與AR眼鏡。然而，現有方法面臨重大挑戰，包括魚眼鏡頭的變形、嚴重的自遮擋，以及視野範圍外的身體部位，這些問題都會降低姿態估計的準確度。此外，許多基於深度學習的方法需要高計算資源與複雜的模型架構設計，使其難以在邊緣設備上實時部署。
在本論文中，我們提出了一個適用於邊緣設備的實時雙目自我視角三維人體姿態估計系統。我們的系統引入了一個注意力引導特徵提取器（AFE），利用多尺度二維熱圖與人體遮罩注意力機制來增強特徵學習。此外，我們開發了一種立體關節混合器（SJM），這是一種簡單的多層感知機（MLP）模型，可整合雙目視覺特徵，同時保持計算效率與準確性。為了提升在真實環境中的適應性，我們引入無監督領域自適應（UDA），包括人體先驗約束與對抗性領域分類器訓練，以減少合成數據與真實世界數據之間的領域差距。 我們在Xilinx Zynq UltraScale+ MPSoC ZCU104 FPGA上部署了該系統，並在數據集推理與攝影機串流模式下實現了24–30 FPS的實時推理。此技術為可擴展的實時自我視角姿態估計奠定了基礎，並能夠提升VR/AR互動、人機介面（HCI）以及運動分析應用的效能。 | zh_TW |
| dc.description.abstract | Egocentric 3D human pose estimation enables natural and immersive interaction in virtual reality (VR) and augmented reality (AR) applications, eliminating the need for external cameras or handheld controllers. This approach provides a compact and mobile solution for full-body motion capture, allowing seamless integration with head-mounted display (HMD) and AR glasses. However, existing methods face significant challenges, including fisheye lens distortion, severe self-occlusion, and out-of-view body parts, which degrade estimation accuracy. Furthermore, many deep learning-based approaches require high computational resources and complex model architecture design, making real-time deployment on edge devices impractical.
In this thesis, we propose a real-time stereo egocentric 3D human pose estimation system optimized for edge-device deployment. Our system introduces an Attention-Guided Feature Extractor (AFE) that utilizes multi-scale 2D heatmaps and human mask attention to enhance feature learning. Additionally, we develop a Stereo Joint Mixer (SJM), a simple MLP-based model that integrates stereo visual features while preserving computational efficiency and accuracy. To improve robustness in real-world environments, we incorporate unsupervised domain adaptation (UDA), including human prior constraints and adversarial domain classifier training, reducing the domain gap between synthetic and real-world data. We implement the system on the Xilinx Zynq UltraScale+ MPSoC ZCU104 FPGA, achieving real-time inference at 24-30 FPS in both dataset inference and camera streaming modes. This technology paves the way for scalable, real-time egocentric pose estimation, enabling enhanced VR/AR interaction, human-computer interfaces (HCI), and motion analysis applications. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-11-26T16:27:47Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-11-26T16:27:47Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Master’s Thesis Acceptance Certificate i
Acknowledgement iii Chinese Abstract v Abstract vii Contents ix List of Figures xiii List of Tables xv 1 Introduction 1 1.1 Egocentric 3D Human Pose Estimation 2 1.2 Challenges 2 1.3 Contribution 3 1.4 Thesis Organization 5 2 Related Work 7 2.1 3D Human Pose Estimation 7 2.2 Egocentric 3D Human Pose Estimation 8 2.2.1 Monocular Egocentric 3D Human Pose Estimation 9 2.2.2 Stereo Egocentric 3D Human Pose Estimation 11 2.3 MLP-Based Architecture 14 2.4 Unsupervised Domain Adaptation (UDA) 15 3 Method 17 3.1 Architecture Overview 17 3.2 Attention-Guided Feature Extractor (AFE) 18 3.2.1 Multi-Scale 2D Heatmaps Attention 19 3.2.2 Human Mask Attention 21 3.2.3 Local Attention Loss 21 3.3 Stereo Joint Mixer (SJM) 23 3.4 Domain Adaptation Training Framework 24 3.4.1 Human Prior Loss 24 3.4.2 Adversarial Domain Classifier Training 24 4 Experiments 27 4.1 Datasets 27 4.1.1 UnrealEgo Dataset 27 4.1.2 UnrealEgo-RW (Real-World) Dataset 28 4.1.3 Our RealEgo (Unlabeled) Dataset 29 4.2 Training and Implementation Details 31 4.3 Main Results 32 4.4 Qualitative Results 35 4.5 Ablation Study 38 4.5.1 Location Feature Types 40 4.5.2 Local Attention Loss 41 4.5.3 Multi-Scale 2D Heamaps 42 4.5.4 Separation of Image Feature and Location Feature 43 4.5.5 UDA Loss Types 43 4.5.6 Computational Efficiency 44 5 System 45 5.1 System Overview 45 5.2 Implementation Details 46 5.2.1 Tools and Frameworks 46 5.2.2 Deployment Workflow 51 5.2.3 Demo Code Implementation 52 5.3 Performance Evaluation 54 6 Conclusion 59 Reference 61 | - |
| dc.language.iso | en | - |
| dc.subject | 三維人體姿態估計 | - |
| dc.subject | 自我視角 | - |
| dc.subject | 多層感知器 | - |
| dc.subject | 非監督領域自適應 | - |
| dc.subject | 頭戴顯示裝置 | - |
| dc.subject | 擴增實境 | - |
| dc.subject | 虛擬實境 | - |
| dc.subject | 3D Human Pose Estimation | - |
| dc.subject | Egocentric | - |
| dc.subject | Multilayer Perceptron | - |
| dc.subject | Unsupervised Domain Adaptation | - |
| dc.subject | Head-Mounted Display | - |
| dc.subject | Augmented Reality | - |
| dc.subject | Virtual Reality | - |
| dc.subject | ZCU104 | - |
| dc.title | 基於注意力引導特徵與領域自適應學習之實時雙目自我視角三維人體姿態估計系統 | zh_TW |
| dc.title | Real-Time Stereo Egocentric 3D Human Pose Estimation System with Attention-Guided Features and Domain-Adaptive Learning | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 114-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 施吉昇;陳冠文;林淵翔 | zh_TW |
| dc.contributor.oralexamcommittee | Chi-Sheng Shih;Kuan-Wen Chen;Yuan-Hsiang Lin | en |
| dc.subject.keyword | 三維人體姿態估計,自我視角多層感知器非監督領域自適應頭戴顯示裝置擴增實境虛擬實境 | zh_TW |
| dc.subject.keyword | 3D Human Pose Estimation,EgocentricMultilayer PerceptronUnsupervised Domain AdaptationHead-Mounted DisplayAugmented RealityVirtual RealityZCU104 | en |
| dc.relation.page | 71 | - |
| dc.identifier.doi | 10.6342/NTU202504485 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2025-09-17 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 電子工程學研究所 | - |
| dc.date.embargo-lift | 2025-11-27 | - |
| 顯示於系所單位： | 電子工程學研究所 | |
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| ntu-114-1.pdf | 30.55 MB | Adobe PDF | 檢視/開啟 |
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