Human Pose Estimation from Monocular Images
Zusammenfassung
Abstract
This dissertation deals with the problem of capturing human motions and poses using a single camera. The frst part of the thesis proposes two closely related approaches for the 3D reconstruction of human motions from image sequences. To resolve inherent ambiguities in monocular 3D reconstruction the main idea of this part is to exploit temporal properties of human motions in combination with a human body model learned from training data. The second part of the thesis tackles the problem of reconstructing a human pose from a single image. A human body model is learned by training a deep neural network that covers nonlinearities and anthropometric constraints.
C O N T E N T S
1 Introduction ….. 1
1.1 Applications and Commercial Systems . . . . . . . . . . . 1
1.2 Image-based Motion Capture . . . . . . . . . . . . . . . . 2
1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3.1 Time Consistent Human Motion Reconstruction . 6
1.3.2 RepNet . . . . . . . ...
Schlagworte
- Kapitel Ausklappen | EinklappenSeiten
- I–XIV
- 1–16 1 Introduction 1–16
- 1.1 Applications and Commercial Systems
- 1.2 Image-based Motion Capture
- 1.3 Contributions
- 1.3.1 Time Consistent Human Motion Reconstruction
- 1.3.2 RepNet
- 1.4 Structure of the Thesis
- 1.5 List of Publications
- 1.5.1 Human Motion Capture
- 1.5.2 Other Publications
- 17–21 2 Related Work 17–21
- 2.1 Non-rigid Structure-from-Motion
- 2.2 Single Image Approaches
- 2.2.1 Reprojection Error Optimization
- 2.2.2 Direct Inference using Neural Networks
- 2.3 Time Consistent Human Motion Capture
- 22–39 3 Fundamentals 22–39
- 3.1 Camera Models
- 3.1.1 Projective Transformations
- 3.1.2 Intrinsic Parameters
- 3.1.3 Extrinsic Parameters
- 3.1.4 Simplified Camera Models
- 3.2 Human Pose Representations
- 3.2.1 Coordinate-based Representations
- 3.2.2 Surface Mesh-based Representations
- 3.2.3 Subspaces of Human Poses
- 3.3 Non-Rigid Structure from Motion
- 3.4 Error Metrics
- 3.5 Datasets
- 40–79 4 Exploiting Temporal Properties 40–79
- 4.1 Periodic and Non-periodic Constraints
- 4.1.1 Factorization model
- 4.1.2 Camera Parameter Estimation
- 4.1.3 Periodic Motion
- 4.1.4 Non-Periodic Motion
- 4.1.5 Algorithm
- 4.1.6 Experimental Results
- 4.1.7 Conclusion
- 4.2 A Novel Kinematic Chain Space
- 4.2.1 Estimating Camera and Shape
- 4.2.2 Kinematic Chain Space
- 4.2.3 Trace Norm Constraint
- 4.2.4 Camera
- 4.2.5 Algorithm
- 4.2.6 Experiments
- 4.2.7 Conclusion
- 80–96 5 Single Image Reconstruction Using Adversarial Training 80–96
- 5.1 Method
- 5.2 Pose and Camera Estimation
- 5.3 Reprojection Layer
- 5.4 Critic Network
- 5.5 Camera
- 5.6 Data Preprocessing
- 5.7 Training
- 5.8 Results
- 5.8.1 Quantitative Evaluation on Human3.6M
- 5.8.2 Quantitative Evaluation on MPI-INF-3DHP
- 5.8.3 Plausibility of the Reconstructions
- 5.8.4 Noisy observations
- 5.8.5 Qualitative Evaluation
- 5.8.6 Conclusion
- 97–100 6 Conclusions 97–100
- 101–116 Bibliography 101–116
