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基于图像视频的三维建模是3D数字技术的核心内容，可以重建真实3D场景和人物，广泛应用于机器人及自动驾驶等领域，属于跨学科研究领域，具有很高的研究和应用价值。本书围绕图像视频三维建模的最新研究技术和方法展开，重点关注具有挑战性问题进行了系统研究和介绍，包括3D物体建模、3D人脸面部建模、3D人体姿态建模及通用建模的相关学习算法，是一本系统介绍三维建模先进方法的研究专著。本书中描述的所有算法都来自我们的研究成果，与最先进方法进行了比较，验证了有效性和先进性。本书将使人工智能及信息计算机等领域的研究人员、专业人士和研究生受益，对跨学科研究也非常有用。

Chapter 1  Introduction	1
1.1  3D Object Modeling	2
1.1.1  Single-View 3D Reconstruction	2
1.1.2  Multi-View 3D Reconstruction Method	3
1.2  3D Face Modeling	5
1.2.1  3D Face Keypoint Detection	5
1.2.2  3D Face Reconstruction	6
1.3  3D Human Body Modeling	9
1.3.1  3D Human Pose Estimation	9
1.3.2  3D human Body Reconstruction	10
1.4  3D Reconstruction Modeling	12
1.5  Outline of the Work	13
Bibliography	14
Chapter 2  3D Object Modeling	17
2.1  Single-View 3D Object Modeling	18
2.1.1  Multi-Scale Edge-Guided Learning for 3D Reconstruction	18
2.1.2  Multi-Granularity Relationship Reasoning Network for 
High-Fidelity 3D Shape Reconstruction	44
2.1.3  3D Shape Reconstruction Based on Dynamic 
Multi-Branch Information Fusion	67
2.1.4  Hierarchical Feature Learning Network for 3D Object 
Reconstruction	78
2.2  Multi-View 3D Object Modeling	94
2.2.1  High-Resolution Multi-View Stereo with Dynamic Depth 
Edge Flow	94
2.2.2  Global Contextual Complementary Network for Multi-View 
Stereo	103
2.2.3  Attention-Guided Multi-View Stereo Network for 
Depth Estimation	114
2.2.4  Self-Supervised Edge Structure Learning for Multi-View 
Stereo and Parallel Optimization	126
2.2.5  Layered Decoupled Complementary Networks for 
Multi-View Stereo	138
2.2.6  Global Balanced Networks for Multi-View Stereo	149
Bibliography	157
Chapter 3  3D Face Keypoint Detection	172
3.1  Learning Relation-Sensitive Structured Network for 
Robust Face Alignment	173
3.1.1  Introduction	173
3.1.2  Proposed Method	174
3.1.3  Experiments	178
3.1.4  Conclusion	181
3.2  Multi-Agent Deep Collaboration Learning for Face Alignment 
under Different Perspectives	182
3.2.1  Introduction	182
3.2.2  Proposed Method	184
3.2.3  Experiments	186
3.2.4  Conclusion	189
3.3  Towards Accurate 3D Face Alignment under Extreme 
Scenarios Via Multi-Granularity Perturbation Relearning	190
3.3.1  Introduction	190
3.3.2  Proposed Method	192
3.3.3  Loss Function	196
3.3.4  Experiments	197
3.3.5  Conclusion	200
Bibliography	201
Chapter 4  3D Face Reconstruction	205
4.1  Towards Rich-Detail 3D Face Reconstruction and Dense 
Alignment via Multi-Scale Detail Augmentation	206
4.1.1  Introduction	206
4.1.2  Proposed Method	207
4.1.3  Experiments	211
4.1.4  Conclusion	214
4.2  Multi-Attribute Regression Network for Face Reconstruction	215
4.2.1  Introduction	215
4.2.2  Proposed Method	217
4.2.3  Experiments	220
4.2.4  Conclusion	227
4.3  Geometry Normal Consistency Loss for 3D Face 
Reconstruction and Dense Alignment	228
4.3.1  Introduction	228
4.3.2  Proposed Method	230
4.3.3  Experiments	234
4.3.4  Conclusion	238
4.4  Complementary Learning Network for 3D Face 
Reconstruction and Alignment	238
4.4.1  Introduction	238
4.4.2  Proposed Method	240
4.4.3  Experiments	243
4.4.4  Conclusion	249
4.5  Graph Structure Reasoning Network for Face Alignment and 
Reconstruction	250
4.5.1  Introduction	250
4.5.2  Proposed Method	252
4.5.3  Experiments	256
4.5.4  Conclusion	260
4.6  Unsupervised Shape Enhancement and Factorization Machine 
Network for 3D Face Reconstruction	261
4.6.1  Introduction	261
4.6.2  Proposed Method	263
4.6.3  Experiments	266
4.6.4  Conclusion	270
4.7  A Detail Geometry Learning Network for High-Fidelity Face 
Reconstruction	271
4.7.1  Introduction	271
4.7.2  Proposed Method	273
4.7.3  Experiments	277
4.7.4  Conclusion	281
4.8  A Bi-Directional Optimization Network for De-Obscured 
3D High-Fidelity Face Reconstruction	282
4.8.1  Introduction	282
4.8.2  Proposed Method	284
4.8.3  Experiments	289
4.8.4  Conclusion	294
Bibliography	294
Chapter 5  3D Human Pose Estimation	304
5.1  Multi-Hybrid Extractor Network for 3D Human Pose Estimation	305
5.1.1  Introduction	305
5.1.2  Proposed Method	306
5.1.3  Experiments	310
5.1.4  Conclusion	312
5.2  3D Human Pose Estimation Based on Center of Gravity	312
5.2.1  Introduction	312
5.2.2  Proposed Method	315
5.2.3  Experiments	319
5.2.4  Conclusion	324
5.3  Edge-Angle Structure Constraint Loss for 3D Human Pose Estimation	324
5.3.1  Introduction	324
5.3.2  Related Works	325
5.3.3  Proposed Method	326
5.3.4  Experiments	329
5.3.5  Conclusion	333
Bibliography	333
Chapter 6  3D Human Body Reconstruction	339
6.1  Two-Stage Co-Segmentation Network Based on Discriminative 
Representation for Recovering Human Mesh from Videos	340
6.1.1  Introduction	340
6.1.2  Related Works	342
6.1.3  Proposed Method	343
6.1.4  Experiments	350
6.1.5  Conclusion	356
6.2  Frame-Level Feature Tokenization Learning for Human 
Body Pose and Shape Estimation	356
6.2.1  Introduction	356
6.2.2  Related Works	358
6.2.3  Proposed Method	359
6.2.4  Experiments	363
6.2.5  Conclusion	368
6.3  Time-Frequency Awareness Network for Human Mesh 
Recovery from Videos	369
6.3.1  Introduction and Related Works	369
6.3.2  Proposed Method	371
6.3.3  Experiments	375
6.3.4  Conclusion	378
6.4  Spatio-Temporal Tendency Reasoning for Human Body Pose and 
Shape Estimation from Videos	378
6.4.1  Introduction and Related Works	378
6.4.2  Proposed Method	380
6.4.3  Experiments	385
6.4.4  Conclusion	389
Bibliography	389

Chapter 7  3D Reconstruction Modeling	394
7.1  Replay Attention and Data Augmentation Network for 
3D Face and Object Reconstruction	395
7.1.1  Introduction	395
7.1.2  Related Works	398
7.1.3  Proposed Method	402
7.1.4  Experiments	408
7.1.5  Conclusion	417
7.2  A Lightweight Grouped Low-Rank Tensor Approximation 
Network for 3D Mesh Reconstruction from Videos	417
7.2.1  Introduction	417
7.2.2  Proposed Method	420
7.2.3  Experiments	423
7.2.4  Conclusion	429
Bibliography	429

3D modeling based on image and video is the core content of 3D digital technology, which belongs to the interdisciplinary research field and has high research and application value. 3D modeling based on image and video can reconstruct real 3D scenes and characters similar to image and video. It can be applied to various scenes, which has been widely studied and applied. Recently, 3D modeling based on image and video has attracted increasing research interest, and researchers have conducted extensive research. There are still many challenging tasks to be completed.
This book revolves around the latest research technologies and methods for 3D modeling of image and video, focusing on challenging issues and conducting systematic research and introduction on 3D modeling of image and video. It introduces relevant algorithms for 3D object modeling, 3D facial modeling, and 3D human pose modeling. In addition, the research on general algorithms in 3D recon-struction is also introduced. All the algorithms described in this book are derived from our research findings and have been compared with state-of-the-art methods and validated to be effective through experiments. Firstly, this book comprehensively introduces the research work on 3D modeling based on image and video. Then, this book introduces our proposed algorithm for 3D object reconstruction, 3D facial keypoint detection and alignment, 3D facial reconstruction, 3D human pose estimation, and 3D human reconstruction separately. Finally, this book introduces the research on general algorithms in 3D reconstruction. This book will benefit the researchers, professionals, graduates and postgraduates working in the field of artificial intelligence, information and computer, etc. This book will also be very useful for interdisciplinary research.
Our team has been engaged in machine vision 3D modeling for nearly 20 years, and we have received the grant support (Grant No. 62062059, 61662059) from the National Natural Science Foundation of China (NSFC). We appreciate the strong and consistent support of the National Natural Science Foundation of China. Thanks to our team members Xiong Tuo, Li Lei, Zhang Boyang, Zhang Xitie, Zhou Zhiyuan, Zhu Congcong, Sun Penghui, Xu Hao, Wang Xing, Ma Kehua, Tian Wenrui, Zhang Jiaqiang, Li Xiangzheng, Zhang Jianjun, Lin Kui, Wu Kangbo, Li Xinyu, Zheng Xing, Cao Hu, Cao Yongrong, Yang Xiaoxiao, Li Pan, Lin Lei, Wang Bin, Yuan Zhixiang, Yang Leyang, Jia Meining, Peng Yuxin, Zhang Shengjia, Lu Wenlong, Wang Bing, Duan Zhijian, Zeng Rongyang, Zhang Xiang, Peng Ruijie, and others for their works. Special thanks to Professor Wang Xueming and Teacher Liu Zhihong. Thanks to the Publishing House of Electronics Inelustry for their strong support in the publication of this book.

Wu Suping
July 2024

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三维建模学习算法（英文版）