Pattern recognition and computer vision . Part II : 4th Chinese Conference, PRCV 2021, Beijing, China, October 29-November 1, 2021, Proceedings / / Huimin Ma [and seven others] (editors)

• Record Nr.: UNISA996464410803316
• Pubbl/distr/stampa: Cham, Switzerland : , : Springer, , [2021]; ©2021
• ISBN: 3-030-88007-9
• Descrizione fisica: 1 online resource (694 pages)
• Collana: Lecture notes in computer science ; ; 13020
• Disciplina: 006.4
• Soggetti: Pattern recognition systems; Computer vision
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Organization -- Contents - Part II -- Computer Vision, Theories and Applications -- Dynamic Fusion Network for Light Field Depth Estimation -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 The Overall Architecture -- 3.2 Pyramid ConvGRU -- 3.3 Multi-modal Dynamic Fusion Module (MDFM) -- 4 Experiments -- 4.1 Experiments Setup -- 4.2 Ablation Studies -- 4.3 Comparison with State-of-the-arts -- 5 Conclusion -- References -- Metric Calibration of Aerial On-Board Multiple Non-overlapping Cameras Based on Visual and Inertial Measurement Data -- 1 Introduction -- 2 Related Works -- 3 Metric Calibration Based on Visual and Inertial Measurement Data -- 3.1 Notation and Problem Formulation -- 3.2 Relative Pose Estimation via Structure from Motion -- 3.3 Inertial Measurement Data Based Metric Scale Factor Estimation -- 4 Experimental Results -- 4.1 Equipment -- 4.2 Metric Calibration of the Aerial On-Board Non-overlapping Camera System -- 4.3 Metric Calibration of an Industrial Non-overlapping Camera System -- 4.4 Experiments of Applications for Object Metric 3D Reconstruction -- 5 Conclusions -- References -- SEINet: Semantic-Edge Interaction Network for Image Manipulation Localization -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Cross Interaction Pattern -- 3.2 Aggregate Interaction Module -- 3.3 Bidirectional Fusion Module -- 3.4 Training Loss -- 4 Experiments -- ; 4.1 Datasets and Implementation Details -- 4.2 Evaluation Metrics -- 4.3 Ablation Studies -- 4.4 Robustness Analysis -- 4.5 Comparing with State-of-the-Art -- 5 Conclusion -- References -- Video-Based Reconstruction of Smooth 3D Human Body Motion -- 1 Introduction -- 2 Related Work -- 2.1 3D Human Mesh from Single Images -- 2.2 3D Human Mesh from Video -- 2.3 GANs for Modeling -- 3 Approach -- 3.1 3D Body Representation -- 3.2 Temporal Encoder.; 3.3 Constraint Loss -- 3.4 Motion Discriminator -- 4 Experiments -- 4.1 Implement Details -- 4.2 Comparison to Other Methods -- 4.3 Ablation Experiments -- 5 Conclusion -- References -- A Unified Modular Framework with Deep Graph Convolutional Networks for Multi-label Image Recognition -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Image Feature Extraction Module -- 3.2 Label Semantic Extraction Module -- 3.3 Prediction Results and Training Scheme -- 4 Experiments -- 4.1 Evaluation Metrics -- 4.2 Implementation Details -- 4.3 Experimental Results -- 4.4 Ablation Studies -- 4.5 Adjacency Matrix Visualization -- 5 Conclusion -- References -- 3D Correspondence Grouping with Compatibility Features -- 1 Introduction -- 2 Related Work -- 2.1 3D Correspondence Grouping -- 2.2 Learning for Correspondence Grouping -- 3 Methodology -- 3.1 Compatibility Check -- 3.2 CF Feature Extraction -- 3.3 CF Classification -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Method Analysis -- 4.3 Comparative Results and Visualization -- 5 Conclusions -- References -- Contour-Aware Panoptic Segmentation Network -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Panoptic Contour Branch -- 3.2 Panoptic Segmentation Branch -- 3.3 Structure Loss Function -- 4 Experiments -- 4.1 Dataset -- 4.2 Evaluation Metrics -- 4.3 Implementation Details -- 4.4 Comparisons with Other Methods -- 4.5 Ablative Analysis -- 5 Conclusion -- References -- VGG-CAE: Unsupervised Visual Place Recognition Using VGG16-Based Convolutional Autoencoder -- 1 Introduction -- 2 Realted Work -- 2.1 Handcraft-Based Methods -- 2.2 CNN-Based Methods -- 2.3 AE-Based Methods -- 3 VGG16-Based Convolutional Autoencoder -- 3.1 Model Architecture -- 3.2 Training -- 3.3 Matching -- 4 Experiments -- 4.1 Datasets -- 4.2 State-of-the-Art Approaches -- 4.3 Ground Truth -- 4.4 Comparison and Discussion.; 5 Conclusion -- References -- Slice Sequential Network: A Lightweight Unsupervised Point Cloud Completion Network -- 1 Introduction -- 2 Related Work -- 2.1 3D Learning -- 2.2 3D Completion -- 3 Our Method -- 3.1 Overview -- 3.2 Slicer -- 3.3 Multi-scale Point Encoder -- 3.4 Sequential Predictor -- 3.5 Shape Prediction Decoder -- 3.6 Loss Function -- 4 Experiments -- 4.1 Datasets and Implementation Details -- 4.2 Point Cloud Completion Results -- 4.3 Analysis of Encoder -- 4.4 Robustness to Occlusion -- 4.5 Comparison of Complexity -- 5 Ablation Study -- 6 Conclusion -- References -- From Digital Model to Reality Application: A Domain Adaptation Method for Rail Defect Detection -- 1 Introduction -- 2 Preliminaries -- 3 Method -- 3.1 DT-Based Virtual Data Generation -- 3.2 Dummy-Target Domain -- 3.3 DA-YOLO -- 4 Experiment -- 4.1 Dataset and Evaluation Metrics -- 4.2 Experiment Settings -- 4.3 Experimental Results -- 5 Conclusion -- References -- FMixAugment for Semi-supervised Learning with Consistency Regularization -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 FMixAugment: MixAugment Combined with FMask -- 3.2 Improved Consistency Regularization -- 3.3 Dynamic Growth Threshold -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Experimental Results -- 4.3 Ablation Study -- 5 Conclusion and Future Work -- References -- IDANet: ; Iterative D-LinkNets with Attention for Road Extraction from High-Resolution Satellite Imagery -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Overview -- 3.2 Basic Iteration Module -- 3.3 Iterative Architecture -- 4 Experiment -- 4.1 Datasets -- 4.2 Implementation Details -- 5 Results -- 5.1 Comparison of Road Segmentation Methods -- 5.2 Ablation Experiment -- 5.3 The Influence of Network Iteration -- 6 Conclusion -- References.; Disentangling Deep Network for Reconstructing 3D Object Shapes from Single 2D Images -- 1 Introduction -- 2 Related Works -- 3 Disentangling Deep Network -- 3.1 Network Architecture -- 3.2 Learning Objective Functions -- 3.3 Training Strategy -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Ablation Analysis -- 4.3 3D Reconstruction -- 4.4 Effects of 3D Shape Identity -- 5 Conclusion -- References -- AnchorConv: Anchor Convolution for Point Clouds Analysis -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 AnchorConv -- 3.2 Anchor Reweighting Module -- 3.3 Network Architectures -- 4 Experiments -- 4.1 Classification on ModelNet40 -- 4.2 ShapeNet Part Segmentation -- 4.3 3D Segmentation of Indoor Scene -- 4.4 3D Segmentation of Outdoor Scene -- 4.5 Ablation Study -- 4.6 Qualitative Results -- 5 Conclusion -- References -- IFR: Iterative Fusion Based Recognizer for Low Quality Scene Text Recognition -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Iterative Collaboration -- 3.2 Fusion Module RRF -- 3.3 Loss Functions -- 3.4 Paired Training Data Generate -- 4 Experiments -- 4.1 Datasets and Implementation Details -- 4.2 Ablation Study -- 4.3 Comparisons with State-of-the-Arts -- 5 Conclusion -- References -- Immersive Traditional Chinese Portrait Painting: Research on Style Transfer and Face Replacement -- 1 Introduction -- 2 Related Work -- 2.1 Neural Style Transfer -- 2.2 Face Replacement -- 3 The P-CP Method -- 3.1 Network Architecture -- 3.2 Neural Style Transfer Network -- 3.3 Face Replacement -- 4 Experiment -- 4.1 Comparison of Different Traditional Chinese Painting Styles -- 4.2 Image Detail Exploration and Optimization -- 4.3 Improvement of Face Replacement with Style Transfer -- 5 Conclusion -- References -- Multi-camera Extrinsic Auto-calibration Using Pedestrians in Occluded Environments -- 1 Introduction.; 2 Related Work -- 3 Calibration Based on 3D Positions -- 3.1 3D Head Positions in Local Camera Coordinates -- 3.2 Registration of 3D Point Sets -- 4 Refinement -- 5 Experiments and Results -- 6 Conclusion -- References -- Dual-Layer Barcodes -- 1 Introduction -- 2 Related Work -- 2.1 Steganography -- 2.2 Watermarking -- 2.3 Barcode -- 3 Method -- 3.1 Encoder -- 3.2 Decoder -- 3.3 Noise Layer -- 3.4 Discriminator -- 4 Experiments and Analysis -- 4.1 Dataset and Experimental Setting -- 4.2 Implementation Details -- 4.3 Metrics -- 5 Discussion -- 6 Conclusion -- References -- Graph Matching Based Robust Line Segment Correspondence for Active Camera Relocalization -- 1 Introduction -- 2 Method -- 2.1 System Overview -- 2.2 Robust Line Segment Matching -- 2.3 Active Camera Relocation -- 3 Experiments -- 3.1 Experimental Setup -- 3.2 Analysis of Line Segment Matching -- 3.3 Analysis of Relocalization Accuracy and Convergence Speed -- 3.4 Analysis of Robustness in Hard Scenes -- 4 Conclusion -- References -- Unsupervised Learning Framework for 3D Reconstruction from Face Sketch -- 1 Introduction -- 2 Related Work -- 2.1 Image-to-Image Translation -- 2.2 3D Shape Reconstruction -- 3 Method -- 3.1 Dataset Construction -- 3.2 Network Architecture -- 3.3 Loss Functions -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Quantitative Results and Ablation Study -- 4.3 Qualitative Results -- 5 Conclusion -- References -- HEI-Human: A Hybrid Explicit and Implicit ; Method for Single-View 3D Clothed Human Reconstruction -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Overview -- 3.2 Explicit Model -- 3.3 Implicit Model -- 3.4 Loss Functions -- 4 Experiments -- 4.1 Dataset and Protocol -- 4.2 Training Details -- 4.3 Quantitative Results -- 4.4 Qualitative Results -- 4.5 Ablation Studies -- 5 Conclusions -- References.; A Point Cloud Generative Model via Tree-Structured Graph Convolutions for 3D Brain Shape Reconstruction.

Blender 2.5 character animation cookbook [[electronic resource] ] : 50 great recipes for giving soul to your characters by building high-quality rigs and understanding the principles of movement / / Virgilio Vasconcelos

• Record Nr.: UNINA9910781204603321
• Autore: Vasconcelos Virgilio
• Pubbl/distr/stampa: Birmingham, U.K. : , : Packt Open Source, , 2011
• ISBN: 1-283-34930-2; 9786613349309; 1-84951-321-X
• Edizione: [First edition]
• Descrizione fisica: 1 online resource (308 pages)
• Disciplina: 006.6; 006.696
• Soggetti: Computer animation; Three-dimensional display systems; Computer graphics
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: "Quick answers to common problems"--Cover.; Includes index.
• Nota di contenuto: Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Get Rigging; Introduction; Defining good orientations for your bones; Using separate bone chains for different tasks; Customizing shapes and colors for your bones; Using corrective shape keys; Making an IK-FK switcher; Tips on weight painting your character; Chapter 2: Rigging the Torso; ; Introduction; How to create a stretchy spine; Rigging the pelvis; Making your character breathe; Controlling the neck and head; Chapter 3: Eying Animation; Introduction; How to control where your characters look at The eyelids controllers; Controlling the pupils; Chapter 4: Poker Face? Facial Rigging; Introduction; Adding expressions using Shape Keys; Face controls with lattices; Creating the jaw controller; Control your tongue; Chapter 5: Hands Down! The Limbs Controllers; Introduction; Controlling fingers; Creating IK legs with a three-pivot foot; Stretch those limbs!; Setting up the shoulders; Cartoon bending for arms and legs; Different spaces for IK hands; Chapter 6: Blending with the Animation Workflow; Introduction; Animating in layers; Changing between FK and IK in a shot Grasping and throwing objects; Silhouette and mirrored rendering; Tracking animation arcs; Using video for background reference; Working with linked assets and characters; Non-linear animation; Chapter 7: Easy to Say, Hard to Do: Mastering the Basics; Introduction; Adjusting and tracking the timing; Spacing: favoring and easing poses; Anticipating an action; Using squash and stretch; Breaking the symmetry; Chapter 8: Shake That Body: The Mechanics of Body Movement; Introduction; Animating a tennis serve; Heavy metal; Glory for your team: kicking the ball; Run, Forrest! (in cycles)Chapter 9: Spicing it Up: Animation Refinement; Introduction; It's time for secondary actions; Hold, but not still: using moving holds; Animating characters with appendages; Like clay: refining with the AniSculpt technique; Chapter 10: Drama King: Acting in Animation; Introduction; In the blink of an eye; Walking with style; Talking heads (and bodies); Appendix: Planning Your Animation; Introduction; Creating thumbnails with Grease Pencil; Naming conventions; Extremes, Breakdowns, Inbetweens, ones and twos; Index
• Sommario/riassunto: Part of Packt's Cookbook series, each chapter focuses on a different aspect of animation. If you don't have the time to work your way through a long tutorial, then this is the book for you. The step-by-step recipes are independent from each other so you can dip in and out of the book to add great effects as and when you need them. Blender users who already know the basics of adding, modeling and rendering objects within the program, but are eager to learn how to turn a character's mesh into a living creature.