Do not miss the opportunity to specialize with this Advanced master’s degree and position yourself with an advantage to access the best Virtual Reality development projects" 

The Virtual Reality and Computer Vision market is in full expansion, requiring more and more professionals with specific qualifications in this field. In recent years, artificial intelligence, another related technology, has brought about a great revolution in the digital world. Its applications range from computer science to health research and the development of tools such as vehicles, robots or video games. 

For this reason, this Advanced master’s degree of TECH contains everything expected and required for the computer scientist who wants to lead his career towards the creation and virtualization of realistic or fantasy environments. In this program you will develop your most advanced skills in the field of creation and virtualization of 3D models, while perfecting your skills in the most advanced tools in the industry. In this way, you will be able to lead the most ambitious Virtual Reality and Computer Vision projects. 

Throughout the program, the computer scientist will also analyze how machines process the visual information received and how this information can be used, either to improve the relationship of the machine itself with its own environment or to collect data efficiently. Deep Learning, a field in continuous development, is approached in the program from an innovative and practical perspective. The computer scientist will know the most important frameworks and hardware in this regard, as well as their application in the different fields of action of computer vision. 

In addition, the computer scientists will enjoy TECH's 100% online methodology, specially designed so that they can combine this program with all kinds of jobs or responsibilities, since it adapts to their personal circumstances. This allows you the flexibility to pursue this educational program without fixed schedules or compulsory attendance at physical centers. Therefore, you have the freedom to distribute the educational content according to your own personal obligations, since all the material can be downloaded from any device with internet access.

Get ahead of the rest and enroll in this program to become a true expert in the technologies that are changing the world today"

This Advanced master’s degree in Virtual Reality and Computer Vision contains the most complete and up-to-date educational program on the market. The most important features include:

• The development of case studies presented by experts in Virtual Reality and Computer Vision
• The graphic, schematic, and practical contents with which they are created, provide scientific and practical information on the disciplines that are essential for professional practice
• Practical exercises where self assessment can be used to improve learning
• Its special emphasis on innovative methodologies in virtual reality, 3D animation and computer vision
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection

Delve into Artificial Intelligence and Deep Learning to become a reference in the field of Computer Vision, taking advantage of the most advanced technological tools in the sector"

Its teaching staff includes professionals from the field of information technology, who bring to this program the experience of their work, as well as recognized specialists from leading companies and prestigious universities.

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide an immersive learning experience designed to prepare for real-life situations.

This program is designed around Problem-Based Learning, whereby the student must try to solve the different professional practice situations that arise throughout the program. For this purpose, the professional will be assisted by an innovative interactive video system created by renowned and experienced experts.

Don't be left behind and get to know all the novelties of Virtual Reality and Computer Vision, taking a decisive step in your professional career by including this Advanced master’s degree in your CV"

Join the most ambitious projects of consolidated companies such as Valve, Microsoft, Google, Ubisoft or Samsung"

The contents of this Advanced master’s degree have been designed by leading international experts in the field, guaranteeing computer scientists access to highly specialized and updated knowledge that will place them as a reference in the sector. In this program you will be able to delve into aspects such as computer vision for the study of space and for content analysis, in the search for patterns and codes, as well as in all fields related to animation and 3D computing in virtual reality environments, with topics and subtopics dedicated to the main tools, working methods, digital resources and forms of team organization. 

Access a library of high quality content that delves into the wide world of Virtual Reality and Computer Vision with detailed videos, simulated cases and complementary readings"

Module 1. Computer Vision 

1.1. Human Perception 

1.1.1. Human Visual System 
1.1.2. Color 
1.1.3. Visible and Non-Visible Frequencies 

1.2. Chronicle of the Computer Vision 

1.2.1. Principles 
1.2.2. Evolution 
1.2.3. The Importance of Computer Vision 

1.3. Digital Image Composition 

1.3.1. The Digital Image 
1.3.2. Types of Images 
1.3.3. Color Spaces 
1.3.4. RGB 
1.3.5. HSV and HSL 
1.3.6. CMY-CMYK 
1.3.7. YCbCr 
1.3.8. Indexed Image 

1.4. Image Acquisition Systems 

1.4.1. Operation of a Digital Camera 
1.4.2. The Correct Exposure for Each Situation 
1.4.3. Depth of Field 
1.4.4. Resolution 
1.4.5. Image Formats 
1.4.6. HDR Mode 
1.4.7. High Resolution Cameras 
1.4.8. High-Speed Cameras 

1.5. Optical Systems 

1.5.1. Optical Principles 
1.5.2. Conventional Lenses 
1.5.3. Telecentric Lenses 
1.5.4. Types of Autofocus Lenses 
1.5.5. Focal Length 
1.5.6. Depth of Field 
1.5.7. Optical Distortion 
1.5.8. Calibration of an Image 

1.6. Illumination Systems 

1.6.1. Importance of Illumination 
1.6.2. Frequency Response 
1.6.3. LED Illumination 
1.6.4. Outdoor Lighting 
1.6.5. Types of Lighting for Industrial Applications. Effects 

1.7. 3D Capture Systems 

1.7.1. Stereo Vision 
1.7.2. Triangulation 
1.7.3. Structured Light 
1.7.4. Time of Flight 
1.7.5. Lidar 

1.8. Multispectrum 

1.8.1. Multispectral Cameras 
1.8.2. Hyperspectral Cameras 

1.9. Non-Visible Near Spectrum 

1.9.1. IR Cameras 
1.9.2. UV Cameras 
1.9.3. Converting From Non-Visible to Visible by Illumination 

1.10. Other Band Spectrums 

1.10.1. X-Ray 
1.10.2. terahertz 

Module 2. Applications and State-of-the-Art 

2.1. Industrial Applications 

2.1.1. Machine Vision Libraries 
2.1.2. Compact Cameras 
2.1.3. PC-Based Systems 
2.1.4. Industrial Robotics 
2.1.5. Pick and Place 2D 
2.1.6. Bin Picking 
2.1.7. Quality Control 
2.1.8. Presence Absence of Components 
2.1.9. Dimensional Control 
2.1.10. Labeling Control 
2.1.11. Traceability 

2.2. Autonomous Vehicles 

2.2.1. Driver Assistance 
2.2.2. Autonomous Driving 

2.3. Computer Vision for Content Analysis 

2.3.1. Filtering by Content 
2.3.2. Visual Content Moderation 
2.3.3. Tracking Systems 
2.3.4. Brand and Logo Identification 
2.3.5. Video Labeling and Classification 
2.3.6. Scene Change Detection 
2.3.7. Text or Credits Extraction 

2.4. Medical Application 

2.4.1. Disease Detection and Localization 
2.4.2. Cancer and X-Ray Analysis 
2.4.3. Advances in Computer Vision given the COVID-19 
2.4.4. Assistance in the Operating Room 

2.5. Spatial Applications 

2.5.1. Satellite Image Analysis 
2.5.2. Computer Vision for the Study of Space 
2.5.3. Mission to Mars 

2.6. Commercial Applications 

2.6.1. Stock Control 
2.6.2. Video Surveillance, Home Security 
2.6.3. Parking Cameras 
2.6.4. Population Control Cameras 
2.6.5. Speed Cameras 

2.7. Vision Applied to Robotics 

2.7.1. Drones 
2.7.2. AGV 
2.7.3. Vision in Collaborative Robots 
2.7.4. The Eyes of the Robots 

2.8. Augmented Reality 

2.8.1. Operation 
2.8.2. Devices 
2.8.3. Applications in the Industry 
2.8.4. Commercial Applications 

2.9. Cloud Computing 

2.9.1. Cloud Computing Platforms 
2.9.2. From Cloud Computing to Production 

2.10. Research and State-of-the-Art 

2.10.1. Commercial Applications 
2.10.2. What's Cooking 
2.10.3. The Future of Computer Vision 

Module 3. Digital Image Processing 

3.1. Computer Vision Development Environment 

3.1.1. Computer Vision Libraries 
3.1.2. Programming Environment 
3.1.3. Visualization Tools 

3.2. Digital image Processing 

3.2.1. Pixel Relationships 
3.2.2. Image Operations 
3.2.3. Geometric Transformations 

3.3. Pixel Operations 

3.3.1. Histogram 
3.3.2. Histogram Transformations 
3.3.3. Operations on Color Images 

3.4. Logical and Arithmetic Operations 

3.4.1. Addition and Subtraction 
3.4.2. Product and Division 
3.4.3. And/Nand 
3.4.4. Or/Nor 
3.4.5. Xor/Xnor 

3.5. Filters 

3.5.1. Masks and Convolution 
3.5.2. Linear Filtering 
3.5.3. Non-Linear Filtering 
3.5.4. Fourier Analysis 

3.6. Morphological Operations 

3.6.1. Erosion and Dilation 
3.6.2. Closing and Opening 
3.6.3. Top_hat and Black hat 
3.6.4. Contour Detection 
3.6.5. Skeleton 
3.6.6. Hole Filling 
3.6.7. Convex Hull 

3.7. Image Analysis Tools 

3.7.1. Edge Detection 
3.7.2. Detection of Blobs 
3.7.3. Dimensional Control 
3.7.4. Color Inspection 

3.8. Object Segmentation 

3.8.1. Image Segmentation 
3.8.2. Classical Segmentation Techniques 
3.8.3. Real Applications 

3.9. Image Calibration 

3.9.1. Image Calibration 
3.9.2. Methods of Calibration 
3.9.3. Calibration Process in a 2D Camera/Robot System 

3.10. Image Processing in a Real Environment 

3.10.1. Problem Analysis 
3.10.2. Image Processing 
3.10.3. Feature Extraction 
3.10.4. Final Results 

Module 4. Advanced Digital Image Processing 

4.1. Optical Character Recognition (OCR) 

4.1.1. Image Pre-Processing 
4.1.2. Text Detection 
4.1.3. Text Recognition 

4.2. Code Reading 

4.2.1. 1D Codes 
4.2.2. 2D Codes 
4.2.3. Applications 

4.3. Pattern Search 

4.3.1. Pattern Search 
4.3.2. Patterns Based on Gray Level 
4.3.3. Patterns Based on Contours 
4.3.4. Patterns Based on Geometric Shapes 
4.3.5. Other Techniques 

4.4. Object Tracking with Conventional Vision 

4.4.1. Background Extraction 
4.4.2. Meanshift 
4.4.3. Camshift 
4.4.4. Optical Flow 

4.5. Facial Recognition 

4.5.1. Facial Landmark Detection 
4.5.2. Applications 
4.5.3. Facial Recognition 
4.5.4. Emotion Recognition 

4.6. Panoramic and Alignment 

4.6.1. Stitching 
4.6.2. Image Composition 
4.6.3. Photomontage 

4.7. High Dinamic Range (HDR) and Photometric Stereo 

4.7.1. Increasing the Dynamic Range 
4.7.2. Image Compositing for Contour Enhancement 
4.7.3. Techniques for the Use of Dynamic Applications 

4.8. Image Compression 

4.8.1. Image Compression 
4.8.2. Types of Compressors 
4.8.3. Image Compression Techniques 

4.9. Video Processing 

4.9.1. Image Sequences 
4.9.2. Video Formats and Codecs 
4.9.3. Reading a Video 
4.9.4. Frame Processing 

4.10. Real Application of Image Processing 

4.10.1. Problem Analysis 
4.10.2. Image Processing 
4.10.3. Feature Extraction 
4.10.4. Final Results

Module 5. 3D Image Processing 

5.1. 3D Imaging 

5.1.1. 3D Imaging 
5.1.2. 3D Image Processing Software and Visualizations 
5.1.3. Metrology Software 

5.2. Open3D 

5.2.1. Library for 3D Data Processing 
5.2.2. Features 
5.2.3. Installation and Use 

5.3. The Data 

5.3.1. Depth Maps in 2D Image 
5.3.2. Pointclouds 
5.3.3. Normal 
5.3.4. Surfaces 

5.4. Visualization 

5.4.1. Data Visualization 
5.4.2. Controls 
5.4.3. Web Display 

5.5. Filters 

5.5.1. Distance Between Points, Eliminate Outliers 
5.5.2. High Pass Filter 
5.5.3. Downsampling 

5.6. Geometry and Feature Extraction 

5.6.1. Extraction of a Profile 
5.6.2. Depth Measurement 
5.6.3. Volume 
5.6.4. 3D Geometric Shapes 
5.6.5. Shots 
5.6.6. Projection of a Point 
5.6.7. Geometric Distances 
5.6.8. Kd Tree 
5.6.9. Features 3D 

5.7. Registration and Meshing 

5.7.1. Concatenation 
5.7.2. ICP 
5.7.3. Ransac 3D 

5.8. 3D Object Recognition 

5.8.1. Searching for an Object in the 3D Scene 
5.8.2. Segmentation
5.8.3. Bin Picking 

5.9. Surface Analysis 

5.9.1. Smoothing 
5.9.2. Orientable Surfaces 
5.9.3. Octree 

5.10. Triangulation 

5.10.1. From Mesh to Point Cloud 
5.10.2. Depth Map Triangulation 
5.10.3. Triangulation of unordered PointClouds 

Module 6. Deep Learning  

6.1. Artificial Intelligence 

6.1.1. Machine Learning 
6.1.2. Deep Learning 
6.1.3. The Explosion of Deep Learning Why Now 

6.2. Neural Networks 

6.2.1. The Neural Network 
6.2.2. Uses of Neural Networks 
6.2.3. Linear Regression and Perception 
6.2.4. Forward Propagation 
6.2.5. Backpropagation 
6.2.6. Feature Vectors 

6.3. Loss Functions 

6.3.1. Loss Functions 
6.3.2. Types of Loss Functions 
6.3.3. Choice of Loss Functions 

6.4. Activation Functions 

6.4.1. Activation Function 
6.4.2. Linear Functions 
6.4.3. Non-Linear Functions 
6.4.4. Output vs. Hidden Layer Activation Functions 

6.5. Regularization and Normalization 

6.5.1. Regularization and Normalization 
6.5.2. Overfitting and Data Augmentation 
6.5.3. Regularization Methods: L1, L2 and Dropout 
6.5.4. Normalization Methods: Batch, Weight, Layer 

6.6. Optimization 

6.6.1. Gradient Descent 
6.6.2. Stochastic Gradient Descent 
6.6.3. Mini Batch Gradient Descent 
6.6.4. Momentum 
6.6.5. Adam 

6.7. Hyperparameter Tuning and Weights 

6.7.1. Hyperparameters 
6.7.2. Batch Size vs. Learning Rate Vs. Step Decay 
6.7.3. Weights 

6.8. Evaluation Metrics of a Neural Network 

6.8.1. Accuracy 
6.8.2. Dice Coefficient 
6.8.3. Sensitivity Vs. Specificity/Recall Vs. Precision 
6.8.4. ROC Curve (AUC) 
6.8.5. F1-Score 
6.8.6. Matrix Confusion 
6.8.7. Cross-Validation 

6.9. Frameworks and Hardware 

6.9.1. Tensor Flow 
6.9.2. Pytorch 
6.9.3. Caffe 
6.9.4. Keras 
6.9.5. Hardware for the Training Phase 

6.10. Creation of a Neural Network-Training and Validation 

6.10.1. Dataset 
6.10.2. Network Construction 
6.10.3. Education 
6.10.4. Visualization of Results 

Module 7. Convolutional Neural Networks and Image Classification 

7.1. Convolutional Neural Networks 

7.1.1. Introduction 
7.1.2. Convolution
7.1.3. CNN Building Blocks 

7.2. Types of CNN Layers 

7.2.1. Convolutional 
7.2.2. Activation 
7.2.3. Batch Normalization 
7.2.4. Polling 
7.2.5. Fully Connected 

7.3. Metrics 

7.3.1. Matrix Confusion 
7.3.2. Accuracy 
7.3.3. Precision 
7.3.4. Recall 
7.3.5. F1 Score 
7.3.6. ROC Curve 
7.3.7. AUC 

7.4. Main Architectures 

7.4.1. AlexNet 
7.4.2. VGG 
7.4.3. Resnet 
7.4.4. GoogleLeNet 

7.5. Image Classification 

7.5.1. Introduction 
7.5.2. Analysis of Data 
7.5.3. Data Preparation 
7.5.4. Model Training 
7.5.5. Model Validation 

7.6. Practical Considerations for CNN Training 

7.6.1. Optimizer Selection 
7.6.2. Learning Rate Scheduler 
7.6.3. Check Training Pipeline 
7.6.4. Training with Regularization 

7.7. Best Practices in Deep Learning 

7.7.1. Transfer Learning 
7.7.2. Fine Tuning 
7.7.3. Data Augmentation 

7.8. Statistical Data Evaluation 

7.8.1. Number of Datasets 
7.8.2. Number of Labels 
7.8.3. Number of Images 
7.8.4. Data Balancing 

7.9. Deployment 

7.9.1. Saving and Loading Models 
7.9.2. Onnx 
7.9.3. Inference 

7.10. Case Study: Image Classification 

7.10.1. Data Analysis and Preparation 
7.10.2. Testing the Training Pipeline 
7.10.3. Model Training 
7.10.4. Model Validation 

Module 8. Object Detection 

8.1. Object Detection and Tracking 

8.1.1. Object Detection 
8.1.2. Case Uses 
8.1.3. Object Tracking 
8.1.4. Case Uses 
8.1.5. Occlusions, Rigid and Non-Rigid Poses 

8.2. Assessment Metrics 

8.2.1. IOU - Intersection Over Union 
8.2.2. Confidence Score 
8.2.3. Recall 
8.2.4. Precision 
8.2.5. Recall-Precisión Curve 
8.2.6. Mean Average Precision (mAP) 

8.3. Traditional Methods 

8.3.1. Sliding Window 
8.3.2. Viola Detector 
8.3.3. HOG 
8.3.4. Non Maximal Supresion (NMS) 

8.4. Datasets 

8.4.1. Pascal VC 
8.4.2. MS Coco 
8.4.3. ImageNet (2014) 
8.4.4. MOTA Challenge 

8.5. Two Shot Object Detector 

8.5.1. R-CNN 
8.5.2. Fast R-CNN 
8.5.3. Faster R-CNN 
8.5.4. Mask R-CNN 

8.6. Single Shot Object Detector 

8.6.1. SSD 
8.6.2. YOLO 
8.6.3. RetinaNet 
8.6.4. CenterNet 
8.6.5. EfficientDet 

8.7. Backbones 

8.7.1. VGG 
8.7.2. ResNet 
8.7.3. Mobilenet 
8.7.4. Shufflenet 
8.7.5. Darknet 

8.8. Object Tracking 

8.8.1. Classical Approaches 
8.8.2. Particulate Filters 
8.8.3. Kalman 
8.8.4. Sort Tracker 
8.8.5. Deep Sort 

8.9. Deployment 

8.9.1. Computing Platform 
8.9.2. Choice of Backbone 
8.9.3. Choice of Framework 
8.9.4. Model Optimization 
8.9.5. Model Versioning 

8.10. Study: People Detection and Tracking 

8.10.1. Detection of People 
8.10.2. Monitoring of People 
8.10.3. Re-Identification 
8.10.4. Counting People in Crowds 

Module 9. Image Segmentation with Deep Learning 

9.1. Object Detection and Segmentation 

9.1.1. Semantic Segmentation 

9.1.1.1. Semantic Segmentation Use Cases 

9.1.2. Instantiated Segmentation 

9.1.2.1. Instantiated Segmentation Use Cases 

9.2. Evaluation Metrics 

9.2.1. Similarities with Other Methods 
9.2.2. Pixel Accuracy 
9.2.3. Dice Coefficient (F1 Score) 

9.3. Cost Functions 

9.3.1. Dice Loss 
9.3.2. Focal Loss 
9.3.3. Tversky Loss 
9.3.4. Other Functions 

9.4. Traditional Segmentation Methods 

9.4.1. Threshold Application with Otsu and Riddlen 
9.4.2. Self-Organized Maps 
9.4.3. GMM-EM Algorithm 

9.5. Semantic Segmentation Applying Deep Learning: FCN 

9.5.1. FCN 
9.5.2. Architecture 
9.5.3. FCN Applications 

9.6. Semantic Segmentation Applying Deep Learning: U-NET 

9.6.1. U-NET 
9.6.2. Architecture 
9.6.3. U-NET Application 

9.7. Semantic Segmentation Applying Deep Learning: Deep Lab 

9.7.1. Deep Lab 
9.7.2. Architecture 
9.7.3. Deep Lab Application 

9.8. Instantiated Segmentation Applying Deep Learning: Mask RCNN 

9.8.1. Mask RCNN 
9.8.2. Architecture 
9.8.3. Application of a Mask RCNN 

9.9. Video Segmentation 

9.9.1. STFCN 
9.9.2. Semantic Video CNNs 
9.9.3. Clockwork Convnets 
9.9.4. Low-Latency 

9.10. Point Cloud Segmentation 

9.10.1. The Point Cloud 
9.10.2. PointNet 
9.10.3. A-CNN 

Module 10. Advanced Image Segmentation and Advanced Computer Vision Techniques 

10.1. Database for General Segmentation Problems 

10.1.1. Pascal Context 
10.1.2. CelebAMask-HQ 
10.1.3. Cityscapes Dataset 
10.1.4. CCP Dataset 

10.2. Semantic Segmentation in Medicine 

10.2.1. Semantic Segmentation in Medicine 
10.2.2. Datasets for Medical Problems 
10.2.3. Practical Applications 

10.3. Annotation Tools 

10.3.1. Computer Vision Annotation Tool 
10.3.2. LabelMe 
10.3.3. Other Tools 

10.4. Segmentation Tools Using Different Frameworks 

10.4.1. Keras 
10.4.2. Tensorflow v2 
10.4.3. Pytorch 
10.4.4. Others 

10.5. Semantic Segmentation Project. The Data, Phase 1 

10.5.1. Problem Analysis 
10.5.2. Input Source for Data 
10.5.3. Data Analysis 
10.5.4. Data Preparation 

10.6. Semantic Segmentation Project. Training, Phase 2 

10.6.1. Algorithm Selection 
10.6.2. Education 
10.6.3. Assessment 

10.7. Semantic Segmentation Project. Results, Phase 3 

10.7.1. Fine Tuning 
10.7.2. Presentation of The Solution 
10.7.3. Conclusions 

10.8. Autoencoders 

10.8.1. Autoencoders 
10.8.2. Autoencoder Architecture 
10.8.3. Noise Elimination Autoencoders 
10.8.4. Automatic Coloring Autoencoder 

10.9. Generative Adversarial Networks (GANs) 

10.9.1. Generative Adversarial Networks (GANs) 
10.9.2. DCGAN Architecture  
10.9.3. Conditional GAN Architecture 

10.10. Enhanced Generative Adversarial Networks 

10.10.1. Overview of the Problem 
10.10.2. WGAN 
10.10.3. LSGAN 
10.10.4. ACGAN

Module 11. 3D Computer Graphics

11.1. 3D Environment 

11.1.1. 3D Program 
11.1.2. Comparison Between Programs 
11.1.3. 3D: Art or Technology 

11.2. Trends in 3D: Realism or Cartoon 

11.2.1. Realistic Trends 
11.2.2. Cartoon Trends 
11.2.3. Realistic vs. Cartoon Aesthetic 

11.3. The Real World and the Virtual World 

11.3.1. 3D Applied to the Real World 
11.3.2. 3D Applied to the Virtual World  
11.3.3. Creating a Virtual World or a Real World 

11.4. 3D for Video Game Production, Film Production and Advertising 

11.4.1. 3D for Video Games 
11.4.2. 3D for Film Production 
11.4.3. 3D for Advertising 

11.5. The 3D Max Interface 

11.5.1. The 3D Scene 
11.5.2. General Interface 
11.5.3. Modifying or Transforming Objects 
11.5.4. Creating a Scene and an Object in 3D 

11.6. Interface and Terminology 

11.6.1. Viewports 
11.6.2. The Menu Bar 
11.6.3. The Main Toolbar 
11.6.4. The Controls  

11.7. Modeling 

11.7.1. 3D Modeling 
11.7.2. Most Commonly Used Specialized Programs 
11.7.3. Graphic Design Applications  
11.7.4. Rendering Engines 

11.8. Application of Materials to the Different Models 

11.8.1. Material Editors 
11.8.2. Creating and Editing Materials  
11.8.3. Applying Light to Materials 
11.8.4. UVW Map Modifier. Mapping Coordinates 
11.8.5. Texture Creation 

11.9. Use of Computer Graphics for the Current Labor Market 

11.9.1. Trends in the Current Labor Market 
11.9.2. Latest Applications 
11.9.3. Use of Computer Graphics   

11.10. Organization of Work 

11.10.1. Creation of a Project  
11.10.2. Folder Structure 
11.10.3. Custom Functionality 

Module 12. Advanced 3D Modeling

12.1. Advanced 3D Modeling Techniques 

12.1.1. Interface Configuration 
12.1.2. The Importance of Observing for Modeling 
12.1.3. Modeling in High 
12.1.4. Organic Modeling for Videogames 
12.1.5. Advanced 3D Object Mapping 

12.2. Advanced 3D Texturing 

12.2.1. Substance Painter Interface 
12.2.2. Materials, Alphas and the Use of Brushes 
12.2.3. Use of Particles 

12.3. Integration with 3D Software and Unreal Engine 

12.3.1. Integration of Unreal Engine in the Designs 
12.3.2. Integration of 3D Models 

12.4. Digital Sculpting

12.4.1. Digital Sculpting with ZBrush 
12.4.2. Interface Configuration and Shortcuts 

12.5. Modeling in ZBrush  

12.5.1. Keyboard Shortcuts 
12.5.2. Handling of Reference Images  
12.5.3. Part Modeling  
12.5.4. Modeling with a Base Mesh 
12.5.5. Study of Human and Animal Musculature 

12.6. The Use of Polypaint  

12.6.1. Advanced Brushes 
12.6.2. Texture
12.6.3. Default Materials 

12.7. Rhetopology 

12.7.1. Rhetopology. Uses 
12.7.2. Creation of Low-Poly Mesh 
12.7.3. Use of Software for Retopology  

12.8. 3D Model Positions 

12.8.1. Reference Image Viewers 
12.8.2. Use of Transpose
12.8.3. Use of Transpose for Models Composed of Different Parts 

12.9. Exporting 3D Models 

12.9.1. Exporting 3D Models 
12.9.2. Texture Generation for Export 
12.9.3. 3D Model Configuration with the Different Materials and Textures  
12.9.4. Preview of the 3D Model 

12.10. Advanced Working Techniques 

12.10.1. Workflow 
12.10.2. Process Organization 
12.10.3.     Production Timing 

Module 13. Advanced Animation and Simulation 

13.1. Software Operation  

13.1.1. Information Management and Work Methodology 
13.1.2. Animation 
13.1.3. Timing and Weighting  
13.1.4. Animation With Basic Objects  

13.2. Direct and Inverse Kinematics 

13.2.1. Inverse Kinematics 
13.2.2. Kinematic Chain 

13.3. Anatomy. Biped Vs. Quadruped 

13.3.1. Biped. Simplicity and Utility 
13.3.2. Quadruped. Simplicity and Utility 
13.3.3. Walking Cycle 
13.3.4. Running Cycle 

13.4. Rig facial and Morpher 

13.4.1. Facial Language. Lip-Sync, Eyes, Focuses of Attention. 
13.4.2. Sequence Editing 

13.5. Applied Animation 

13.5.1. 3D Animation for Cinema and Television 
13.5.2. Animation for Video Games 
13.5.3. Animation for Other Applications 

13.6. Motion Capture with Kinect 

13.6.1. Motion Capture for Animation 
13.6.2. Sequence of Movements 
13.6.3. Integration in Blender 

13.7. Skeleton, Skinning and Setup

13.7.1. Interaction Between Skeleton and Geometry 
13.7.2. Mesh Interpolation 
13.7.3. Animation Weights 

13.8. Acting 

13.8.1. Body Language 
13.8.2. Poses 
13.8.3. Sequence Editing 

13.9. The Shot 

13.9.1. The Camera and the Environment 
13.9.2. Composition of the Shot and the Characters 
13.9.3. Finishes 

13.10. Visual Effects  

13.10.1. Visual Effects and Animation 
13.10.2. Types of Optical Effects 
13.10.3. 3D VFX L 

Module 14. Creative and Conceptual Development. Project Briefing

14.1. Development of the Idea

14.1.1. General Idea
14.1.2. SWOT (Strengths, Weaknesses, Opportunities, Threats and Opportunities)
14.1.3. Anticipation of Problems, Solutions and Project Needs

14.2. Scenarios and Assets

14.2.1. Scenario Design
14.2.2. Design of Assets
14.2.3. Interaction with Levels
14.2.4. Production Planning

14.3. Characters

14.3.1. Character Design
14.3.2. Design of All Character Assets
14.3.3. Interaction with Levels
14.3.4. Production Planning

14.4. Plot Development

14.4.1. General Description of the Game/App
14.4.2. Assignment of Objectives by Levels
14.4.3. Creation of the Bible and Briefing for Developers

14.5. Objectives

14.5.1. Target Audience
14.5.2. Project Positioning
14.5.3. Detection of Potential Competitors

14.6. Design of the Work and Production Plan

14.6.1. Work Flow
14.6.2. File Nomenclature and Folder System
14.6.3. Choice of Work Tools
14.6.4. Resources Required
14.6.5. Timing
14.6.6. Costs

14.7. Music and Sound Effects

14.7.1. Sound Design
14.7.2. Effects Design
14.7.3. Composition and Production

14.8. Production

14.8.1. Start of Production
14.8.2. Supervision and Quality Control
14.8.3. First Built for Test

14.9. Test and Debug

14.9.1. Internal Tests
14.9.2. Laboratory and/or Universal Sample Tests
14.9.3. Customer Testing

14.10. Beta Release

14.10.1. First Built
14.10.2. Publication in Media and Sales Channels
14.10.3. Launch (Communication, Media Plan)
14.10.4. After-Sales Service

Module 15. Video Game Development

15.1. Video Games

15.1.1. Video Games
15.1.2. Errors and Successes in the History of Video Games
15.1.3. Applications in Other Fields and Industries

15.2. Development of Video Games

15.2.1. Production Plan and Development Phases
15.2.2. Development Methodology

15.3. 3D Unity

15.3.1. Unity 3D. Applications
15.3.2. Scripting in Unity 3D
15.3.3. Assets Marketplace

15.4. Physics, Inputs and Other Aspects

15.4.1. Physics in Unity 3D
15.4.2. Particle System
15.4.3. Animation and Animator

15.5. Programming of Physical Behavior

15.5.1. Use and Development of Physical Engines
15.5.2. Specific use of Professional Engines
15.5.3. PhysX

15.6. Specific Programming Techniques

15.6.1. Script Languages
15.6.2. Loading of Resources in the Execution of Windows Games
15.6.3. Performance

15.7. Video Games for Mobile Devices

15.7.1. Multiplatform Developments
15.7.2. Games for IOS Devices
15.7.3. Games for Android Devices

15.8. Augmented Reality

15.8.1. Types of Augmented Reality games
15.8.2. ARkit and ARcore
15.8.3. Vuforia Development

15.9. Artificial Intelligence Programming

15.9.1. Script Languages
15.9.2. Artificial Intelligence Mathematics and Algorithms
15.9.3. Neural Networks

15.10. Distribution and Marketing

15.10.1. The art of Publishing and Promoting a Video Game
15.10.2. The Responsible for Success
15.10.3. Strategies

Module 16. Advanced Unreal Engine

16.1. Programming in Unreal

16.1.1. Unreal and Blueprints
16.1.2. Programming in C++
16.1.3. Use of 3D Models and Animations
16.1.4. Artificial Intelligence

16.2. Advanced Level Construction

16.2.1. Assembling a Level Using Modules
16.2.2. Importing Assets
16.2.3. Configuration for Player Controls

16.3. Lighting in Unreal Engine

16.3.1. Preparing the Environment for Lighting
16.3.2. Types of Lights: Point Lights, Spot Lights, Directional Lights and Skylights
16.3.3. Lightmaps
16.3.4. Static and Dynamic Lights
16.3.5. Resolution Settings

16.4. Complex Shaders

16.4.1. Opaque and Transparent Materials
16.4.2. Static and Dynamic Reflections
16.4.3. Blending of Materials
16.4.4. Special Materials

16.5. The use of Blueprints

16.5.1. Blueprints. Programming Logic
16.5.2. Creating Motion Controls
16.5.3. Creating Interactive Objects

16.6. Creating Cameras

16.6.1. Types of Cameras
16.6.2. Camera Properties
16.6.3. Playable and Cinematic Cameras

16.7. Post Processing Effects

16.7.1. Image Retouching
16.7.2. Effects: Light, Shading, Blurring
16.7.3. Depth of Field

16.8. Multiplayer Games

16.8.1. Network Games
16.8.2. Development of Multiplayer Network Games
16.8.3. Troubleshooting Connectivity Problems

16.9. Production

16.9.1. Development Process
16.9.2. Team Organization
16.9.3. Planning and Methodologies

16.10. Videogame Development and Involved Teams

16.10.1. Collaboration with Designers
16.10.2. Elaboration of the Concept and Design
16.10.3. Producers and Distributors

Module 17. Unity 3D Advanced

17.1. Technical Process

17.1.1. Character Creation and Optimization
17.1.2. Application of Advanced Rhetopologies
17.1.3. High Polygonization Transfers
17.1.4. Photometry

17.2. Character Design

17.2.1. Skills
17.2.2. Anatomy Techniques
17.2.3. Creation and Behavior

17.3. Exporting Skeletal Meshes and Importing into Unreal

17.3.1. Exporting Skeletal Meshes from the 3D Software
17.3.2. Importing Skeletal Meshes in Unreal
17.3.3. Optimization

17.4. Importing Animations

17.4.1. Animation Preparation
17.4.2. Importing Animations
17.4.3. Troubleshooting Import Errors

17.5. Animation Editor

17.5.1. Creating Blend Spaces
17.5.2. Creating Animation Montage
17.5.3. Creating Notifies to Generate Events in an Animation
17.5.4. Creating Shokets for Linking to Objects or Particles

17.6. Physics Applied to a Character or Object

17.6.1. 3D Physics in Unity
17.6.2. Endowing Realism to the Character
17.6.3. The Laws of Physics

17.7. Creation and Simulation of a Physx Ragdoll

17.7.1. Configuration of a Ragdoll
17.7.2. Ragdoll to an Animation Graph
17.7.3. Simulation of a Ragdoll

17.8. Resources for Character Creation

17.8.1. Libraries
17.8.2. Importing and Exporting Library Materials
17.8.3. Handling of Materials

17.9. Work Teams

17.9.1. Channeling of Resources
17.9.2. Asset Processor
17.9.3. Resource Generator

17.10. Requirements for Successful Development

17.10.1. Production for Success
17.10.2. Optimal Development
17.10.3. Essential Requirements

Module 18. Creating Cinematics with Sequencer and Niagara

18.1. The Cameras

18.1.1. Overview of a Scene
18.1.2. Specific View and Details of a Scene
18.1.3. Scene Approach

18.2. Unreal Engine Sequencer Tool

18.2.1. Sequencer
18.2.2. Kinematics Generation
18.2.3. Types of Cameras

18.3. Creating Cinematics with Sequencer

18.3.1. Animation of Cinematic Cameras
18.3.2. Creating and Editing Shots
18.3.3. Playback of Sequenced Animations

18.4. Animated In-Game Objects

18.4.1. Animating Objects in Unreal with Sequencer
18.4.2. Types of Animation
18.4.3. Creating a Turntable

18.5. Particle System with Niagara

18.5.1. Types of Particle Systems
18.5.2. Creating Particle Systems

18.6. Specific Particles

18.6.1. Water Particles
18.6.2. Fire Particles
18.6.3. Explosion Particles
18.6.4. Lightning Particles

18.7. Scripting with Advanced Blueprint

18.7.1. Synchronous and Asynchronous
18.7.2. Creating a Basic AI
18.7.3. Creation of a Nav Mesh
18.7.4. Creation of a Basic Interface with Unreal Motion Graphics

18.8. Optimization of the Game

18.8.1. Rendering Distance
18.8.2. Model Detail Levels
18.8.3. Use of Cull Distance Volumes

18.9. Packaging and Creating an Installable

18.9.1. Packaging Configuration
18.9.2. Packaging the Project
18.9.3. Retouching and Refiners

18.10. Games for Mobile Devices

18.10.1. Peculiarities of Mobile
18.10.2. Specific Assets
18.10.3. Compatible Assets
18.10.4. Digital Platforms

Module 19. Virtual reality

19.1. Virtual reality

19.1.1. Virtual reality
19.1.2. Virtual Reality Applications
19.1.3. The Perception of Virtual Environments, Evaluation, Presence and Immersion

19.2. Input and Output Devices

19.2.1. Input Devices
19.2.2. 3D Audio Rendering
19.2.3. Displays and Other Devices

19.3. Stereoscopic Vision

19.3.1. Stereo Vision
19.3.2. Main Algorithms and Current Formats
19.3.3. HMD Devices

19.4. Haptic Interaction

19.4.1. Haptic Interaction in Virtual Reality
19.4.2. Haptic Rendering Algorithms
19.4.3. Haptic Navigation Methods

19.5. Virtual Reality Oriented Architectures

19.5.1. Architecture and Software of Virtual Reality applications
19.5.2. Design
19.5.3. Virtual Reality Simulators

19.6. Interaction Techniques

19.6.1. Interaction Techniques for Immersive Environments
19.6.2. Main Algorithms and Devices
19.6.3. Brain-Computer Interfaces

19.7. Real-Time Decision Making

19.7.1. Model Organization in REVIT for Use
19.7.2. Enscape Functionalities
19.7.3. Alternatives

19.8. 360° Image

19.8.1. Lumion
19.8.2. Bidirectionality
19.8.3. Lumion Functionalities
19.8.4. 360° Image processing

19.9. 360° Video

19.9.1. Twinmotion
19.9.2. Twinmotion Features
19.9.3. 360° Video Processing

19.10. Virtual Tour

19.10.1. 360° Rendering
19.10.2. 360º Photography
19.10.3. 360° Image Editing
19.10.4. Virtual Tour Creation

Module 20. Software for Virtual Reality Development

20.1. Advanced Virtual Reality Applications

20.1.1. Creation of Advanced VR Applications for Mobiles
20.1.2. Creation of Advanced VR Standalone Applications
20.1.3. Creating Advanced VR Applications for PCs

20.2. Virtual Reality with Unreal Engine

20.2.1. Bidirectionality
20.2.2. Content Organization
20.2.3. Application Development

20.3. Virtual Reality with Unity

20.3.1. Bidirectionality
20.3.2. Content Organization
20.3.3. Application Development

20.4. Virtual Reality Design Coordination

20.4.1. General Aspects to be Taken into Account
20.4.2. Workflows
20.4.3. Data Science

20.5. Extended Reality

20.5.1. Extended Reality
20.5.2. Contextualization
20.5.3. Implementation

20.6. Mixed Reality

20.6.1. Mixed Reality
20.6.2. Uses of Mixed Reality
20.6.3. Types of Mixed Reality Applications

20.7. Mixed Reality Software

20.7.1. Mixed Reality with Unity
20.7.2. Mixed Reality with Unreal Engine
20.7.3. Mixed Reality Devices

20.8. Augmented Reality

20.8.1. Augmented Reality
20.8.2. Creation of Augmented Reality Experiences
20.8.3. Augmented Reality Applications

20.9. Creating Augmented Reality Applications

20.9.1. Augmented Reality with Unity
20.9.2. Augmented Reality with Vuforia
20.9.3. Augmented Reality with Unreal Engine

20.10. Implementation of Virtual Reality

20.10.1. Uses of Virtual Reality
20.10.2. Educational and Training Applications of Virtual Reality
20.10.3. Creating a Useful Virtual Reality Application
20.10.4. Sales Strategy for a Virtual Reality Application 

With the advanced knowledge you will acquire in this Advanced master’s degree you will be able to access jobs in the best video game and 3D design companies"