Position yourself in a booming industry, with the best program in the university landscape that only TECH can offer you” 

Virtual Reality transports us to immersive worlds, allowing experiences ranging from the simulation of complex surgeries to architectural design in real time. The impact of this discipline transcends the technological sphere, as it is shaping the way we live, work and learn. Its constant evolution not only demands professionals trained to implement these tools, but also visionaries capable of expanding their applications to new horizons. 

Computer Vision gives machines the ability to interpret and analyze images and videos, enabling the development of advanced technologies. These include autonomous vehicles, which are revolutionizing transportation, and medical diagnostic platforms, which improve accuracy and efficiency in healthcare. In addition, recent advances in this field, such as multitasking models and generative technologies, are opening up new possibilities in the creation of innovative solutions. Integration with edge computing has also facilitated real-time data processing, which further expands the applications of Computer Vision. For all these reasons, being a professional trained in these disciplines not only opens doors in a technology sector in constant growth, but also allows you to be part of projects that have a real impact on daily life. It also contributes to the development of technologies that continue to transform the way we interact with the world and improve our quality of life. 

The TECH curriculum, together with its 100% online methodology and Relearning learning approach, allows the student to fully concentrate on the key subjects to specialize in these technological areas. In addition, the graduate will have the support of the most specialized faculty and the most updated research in the university field. All this without timetables and from anywhere in the world, which allows students to adapt their studies to their own pace, without interfering with their personal or work commitments. 

The combination of creativity and technology is waiting for you to start developing great solutions with a global impact” 

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

• Practical cases presented by experts in IT 
• 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 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 

Become the best in Virtual Reality and Computer Vision, at your own pace, without schedules and from anywhere in the world”

Its teaching staff includes professionals from the field of journalism, who bring to this program the experience of their work, as well as renowned specialists from reference societies 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.  

Master these technologies with the didactic tools that TECH offers you and start changing lives"

Develop applications and live the most exciting challenges in the world's largest online university"

The Advanced master’s degree curriculum in Virtual Reality and Computer Vision is designed as a comprehensive and advanced academic opportunity in these two key disciplines. The program begins with a solid foundation in the fundamentals of programming, applied mathematics and image processing. Throughout the course, students will delve into the development of virtual environments using state-of-the-art tools. In addition, they will explore advanced simulation and interaction techniques in immersive environments.  

Join TECH and you will begin to transform entertainment with immersive experiences in Computer Vision”   

Module 1. Computer Vision 

1.1. Human Perception 

1.1.1. Human Visual System 
1.1.2. The 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 Covid19 
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 Dynamic 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. 3D Features 

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 Studies 
8.1.3. Object Tracking 
8.1.4. Case Studies 
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-Precision 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 Supression (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. The 3D Industry 

11.1. 3D Industry in Animation and Video Games 

11.1.1. 3D Animation 
11.1.2. 3D Industry in Animation and Video Games 
11.1.3. 3D Animation Future 

11.2. 3D in Video Games 

11.2.1. Video Games. Limitations 
11.2.2. 3D Video Game Development. Difficulties 
11.2.3. Solutions to Video Game Development Difficulties 

11.3. 3D Software for Video Games 

11.3.1. Maya. Pros and Cons 
11.3.2. 3Ds Max. Pros and Cons 
11.3.3. Blender. Pros and Cons 

11.4. Pipeline in 3D Asset Generation for Video Games 

11.4.1. Idea and Assembly from a Modelsheet 
11.4.2. Modeling with Low Geometry and High Detailing 
11.4.3. Projection of Textured Details 

11.5. Key Artistic 3D Styles for Video Games 

11.5.1. Cartoon Style 
11.5.2. Realistic Style 
11.5.3. Cel Shading 
11.5.4. Motion Capture 

11.6. 3D Integration 

11.6.1. 2D Digital World Integration 
11.6.2. 3D Digital World Integration 
11.6.3. Real-World Integration (AR, MR/XR) 

11.7. Key 3D Factors for Different Industries 

11.7.1. 3D in Film and Series 
11.7.2. 3D in Video Games 
11.7.3. 3D in Marketing 

11.8. Render: Real-time Rendering and Pre-Rendering 

11.8.1. Lighting 
11.8.2. Shadow Definition 
11.8.3. Quality vs. Speed 

11.9. 3D Asset Generation in 3D Max 

11.9.1. 3D Max Software 
11.9.2. Interface, Menus, Toolbars 
11.9.3. Controls 
11.9.4. Scene 
11.9.5. Viewports 
11.9.6. Basic Shapes 
11.9.7. Object Generation, Modification and Transformation 
11.9.8. 3D Scene Creation 
11.9.9. 3D Professional Asset Modeling for Video Games 
11.9.10. Material Editors 

11.9.10.1. Creating and Editing Materials 
11.9.10.2. Applying Light to Materials 
11.9.10.3. UVW Map Modifier. Mapping Coordinates 
11.9.10.4. Texture Creation 

11.10. Workspace Organization and Best Practices 

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

Module 12. Art and 3D in the Video Game Industry 

12.1. 3D VR Projects 

12.1.1. 3D Mesh Creation Software 
12.1.2. Image Editing Software 
12.1.3. Virtual Reality 

12.2. Typical Problems, Solutions and Project Needs 

12.2.1. Project Needs 
12.2.2. Possible Problems 
12.2.3. Solutions 

12.3. Aesthetic Line Study for the Artistic Style Generation in Video Games: From Game Design to 3D Art Generation 

12.3.1. Choice of Video Game Recipient. Who We Want to Reach 
12.3.2. Developer's Artistic Possibilities 
12.3.3. Final Definition of the Aesthetic Line 

12.4. Aesthetic Benchmarking and Competitor Analysis 

12.4.1. Pinterest and Similar Sites 
12.4.2. Modelsheet Creation 
12.4.3. Competitor Search 

12.5. Bible Creation and Briefing 

12.5.1. Bible Creation 
12.5.2. Bible Development 
12.5.3. Briefing Development 

12.6. Scenarios and Assets 

12.6.1. Production Asset Planning at Production Levels 
12.6.2. Scenario Design 
12.6.3. Asset Design 

12.7. Asset Integration in Levels and Tests 

12.7.1. Integration Process at All Levels 
12.7.2. Texture. 
12.7.3. Final Touches 

12.8. Characters 

12.8.1. Character Production Planning 
12.8.2. Character Design 
12.8.3. Character Asset Design 

12.9. Character Integration in Scenarios and Tests 

12.9.1. Character Integration Process in Levels 
12.9.2. Project Needs 
12.9.3. Animations 

12.10. 3D Video Game Audio 

12.10.1. Project Dossier Interpretation for Sound Identity Generation of Video Games 
12.10.2. Composition and Production Processes 
12.10.3. Soundtrack Design 
12.10.4. Sound Effect Design 
12.10.5. Voice Design 

Module 13. Advanced 3D 

13.1. Advanced 3D Modeling Techniques 

13.1.1. Interface Configuration  
13.1.2. Modeling Observation  
13.1.3. Modeling in High  
13.1.4. Organic Modeling for Videogames  
13.1.5. Advanced 3D Object Mapping 

13.2. Advanced 3D Texturing 

13.2.1. Substance Painter Interfaces  
13.2.2. Materials, Alphas and Brush Use  
13.2.3. Particle Use 

13.3. 3D Software and Unreal Engine Export 

13.3.1. Unreal Engine Integration in Designs  
13.3.2. 3D Model Integration 
13.3.3. Unreal Engine Texture Application 

13.4. Digital Sculpting 

13.4.1. Digital Sculpting with ZBrush 
13.4.2. First Steps in ZBrush 
13.4.3. Interface, Menus and Navigation 
13.4.4. Reference Images 
13.4.5. Full 3D Modeling of Objects in ZBrush 
13.4.6. Base Mesh Use 
13.4.7. Part Modeling 
13.4.8. 3D Model Export in ZBrush 

13.5. Polypaint Use 

13.5.1. Advanced Brushes 
13.5.2. Texture. 
13.5.3. Default Materials 

13.6. Rheopology 

13.6.1. Rheopology. Use in the Video Game Industry 
13.6.2. Creation of Low-Poly Mesh 
13.6.3. Software Use for Rhetopology 

13.7. 3D Model Positions 

13.7.1. Reference Image Viewers 
13.7.2. Use of Transpose 
13.7.3. Transpose Use for Models Composed of Different Pieces 

13.8. 3D Model Export 

13.8.1. 3D Model Export 
13.8.2. Texture Generation for Exportation 
13.8.3. 3D Model Configuration with the Different Materials and Textures 
13.8.4. Preview of the 3D Model 

13.9. Advanced Working Techniques 

13.9.1. 3D Modeling Workflow 
13.9.2. 3D Modeling Work Process Organization 
13.9.3. Production Effort Estimates 

13.10. Model Finalization and Export for Other Programs 

13.10.1. Workflow for Model Finalization 
13.10.2. Zpluging Exportation 
13.10.3. Possible Files. Advantages and Disadvantages 

 Module 14. 3D Animation 

14.1. Software Operation 

14.1.1. Information Management and Work Methodology 
14.1.2. Animation 
14.1.3. Timing and Weight 
14.1.4.  Animation With Basic Objects 
14.1.5. Direct and Inverse Cinematics 
14.1.6. Inverse Kinematics 
14.1.7. Kinematic Chain 

14.2. Anatomy. Biped vs. Quadruped 

14.2.1. Biped 
14.2.2. Quadruped 
14.2.3. Walking Cycle 
14.2.4. Running Cycle 

14.3. Facial Rig and Morpher 

14.3.1. Facial Language. Lip-Sync, Eyes and Focal Points 
14.3.2. Sequence Editing 
14.3.3. Phonetics. Importance 

14.4. Applied Animation 

14.4.1. 3D Animation for Film and Television 
14.4.2. Animation for Video Games 
14.4.3. Animation for Other Applications 

14.5. Motion Capture with Kinect 

14.5.1. Motion Capture for Animation 
14.5.2. Sequence of Movements 
14.5.3. Blender Integration 

14.6. Skeleton, Skinning and Setup 

14.6.1. Interaction Between Skeleton and Geometry 
14.6.2. Mesh Interpolation 
14.6.3. Animation Weights 

14.7. Acting 

14.7.1. Body Language 
14.7.2. Poses 
14.7.3. Sequence Editing 

14.8. Cameras and Plans 

14.8.1. The Camera and the Environment 
14.8.2. Composition of the Shot and the Characters 
14.8.3. Finishes 

14.9. Visual Special Effects 

14.9.1. Visual Effects and Animation 
14.9.2. Types of Optical Effects 
14.9.3. 3D VFX L 

14.10. The Animator as an Actor 

14.10.1. Expressions 
14.10.2. Actors' References 
14.10.3. From Camera to Program 

Module 15. Unity 3D and Artificial Intelligence Proficiency 

15.1. Video Games. 3D Unity 

15.1.1. Video Games 
15.1.2. Video Games. Errors and Hits 
15.1.3. Video Game Applications in Other Areas and Industries 

15.2. Video Game Development. 3D Unity 

15.2.1. Production Plan and Development Phases 
15.2.2. Development Methodology 
15.2.3. Patches and Additional Content 

15.3. 3D Unity 

15.3.1. Unity 3D. Applications 
15.3.2. Unity 3D Scripting 
15.3.3. Asset Store and Third-Party Plugins 

15.4. Physics, Inputs 

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

15.5. Unity Prototyping 

15.5.1. Blocking and Colliders 
15.5.2. Pre-Fabs 
15.5.3. Scriptable Objects 

15.6. Specific Programming Techniques 

15.6.1. Singleton Model 
15.6.2. Loading of Resources in the Execution of Windows Games 
15.6.3. Performance and Profiler 

15.7. Video Games for Mobile Devices 

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

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. Artificial Intelligence Algorithms 
15.9.2. Finite State Machines 
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. 2D and 3D Video Game Development 

16.1. Raster Graphic Resources 

16.1.1. Sprites 
16.1.2. Atlas 
16.1.3. Texture 

16.2. Interface and Menu Development 

16.2.1. Unity GUI 
16.2.2. Unity UI 
16.2.3. UI Toolkit 

16.3. Animation System 

16.3.1. Animation Curves and Keys 
16.3.2. Applied Animation Events 
16.3.3. Modifiers 

16.4. Materials and Shaders 

16.4.1. Material Components 
16.4.2. RenderPass Types 
16.4.3. Shaders 

16.5. Particles 

16.5.1. Particle Systems 
16.5.2. Transmitters and Sub-Transmitters 
16.5.3. Scripting 
16.5.4. Lighting 

16.6. Lighting Modes 

16.6.1. Light Baking 
16.6.2. Light Probes 

16.7. Mecanim 

16.7.1. State Machines, SubState Machines and Transitions between Animations 
16.7.2. Blend Trees 
16.7.3. Animation Layers and IK 

16.8. Cinematic Finish 

16.8.1. Timeline 
16.8.2. Post-Processing Effects 
16.8.3. Universal Render and High-Definition Render Pipeline 

16.9. Advanced VFX 

16.9.1. VFX Graph 
16.9.2. Shader Graph 
16.9.3. Pipeline Tools 

16.10. Audio Components 

16.10.1. Audio Source and Audio Listener 
16.10.2. Audio Mixer 
16.10.3. Audio Spatializer 

 Module 17. Programming, Mechanics Generation and Video Game Prototyping Techniques 

17.1. Technical Process 

17.1.1. Low-Poly and High-Poly  Unity Models 
17.1.2. Material Settings 
17.1.3. High-Definition Render Pipeline 

17.2. Character Design 

17.2.1. Movement 
17.2.2. Collider Design 
17.2.3. Creation and Behavior 

17.3. Importing Skeletal Meshes into Unity 

17.3.1. Exporting Skeletal Meshes from the 3D Software 
17.3.2. Skeletal Meshes in Unity 
17.3.3. Anchor Points for Accessories 

17.4. Importing Animations 

17.4.1. Animation Preparation 
17.4.2. Importing Animations 
17.4.3. Animator and Transitions 

17.5. Animation Editor 

17.5.1. Blend Spaces Creation 
17.5.2. Creating Animation Montage 
17.5.3. Editing Read-Only Animations 

17.6. Ragdoll Creation and Simulation 

17.6.1. Configuration of a Ragdoll 
17.6.2. Ragdoll to an Animation Graph 
17.6.3. Simulation of a Ragdoll 

17.7. Resources for Character Creation 

17.7.1. Libraries 
17.7.2. Importing and Exporting Library Materials 
17.7.3. Handling of Materials 

17.8. Work Teams 

17.8.1. Hierarchy and Work Roles 
17.8.2. Version Control Systems 
17.8.3. Conflict Resolution 

17.9. Requirements for Successful Development 

17.9.1. Production for Success 
17.9.2. Optimal Development 
17.9.3. Essential Requirements 

17.10. Publication Packaging 

17.10.1. Player Settings 
17.10.2. Build 
17.10.3. Installer Creation 

Module 18. VR Immersive Game Development 

18.1. Uniqueness of VR 

18.1.1. Traditional Video Games and VR. Differences 
18.1.2. Motion Sickness: Smoothness vs. Effects 
18.1.3. Unique VR Interactions 

18.2. Interaction 

18.2.1. Events 
18.2.2. Physical Triggers 
18.2.3. Virtual vs. Real World 

18.3. Immersive Locomotion 

18.3.1. Teletransportation 
18.3.2. Arm Swinging 
18.3.3. Forward Movement with and without Facing 

18.4. VR Physics 

18.4.1. Grippable and Throwable Objects 
18.4.2. Weight and Mass in VR 
18.4.3. Gravity in VR 

18.5. UI in VR 

18.5.1. Positioning and Curvature of UI Elements 
18.5.2. VR Menu Interaction Modes 
18.5.3. Best Practices for Comfortable Experiences 

18.6. VR Animation 

18.6.1. Animated Model Integration in VR 
18.6.2. Animated Objects and Characters vs. Physical Objects 
18.6.3. Animated vs. Procedural Transitions 

18.7. Avatars 

18.7.1. Avatar Representation from Your Own Eyes 
18.7.2. External Representation of Avatars 
18.7.3. Inverse Cinematic and Procedural Avatar Animation 

18.8. Audio 

18.8.1. Configuring Audio Sources and Audio Listeners for VR 
18.8.2. Effects Available for More Immersive Experiences 
18.8.3. VR Audio Spatializer 

18.9. VR and AR Project Optimization 

18.9.1. Occlusion Culling 
18.9.2. Static Batching 
18.9.3. Quality Settings and Render Pass Types 

18.10. Practice: VR Escape Room 

18.10.1. Experience Design 
18.10.2. Scenario Layout 
18.10.3. Mechanic Development 

Module 19. Professional Audio for 3D VR Video Games 

19.1. Professional 3D Video Games Audio 

19.1.1. Video Game Audio 
19.1.2. Audio Style Types in Current Video Games 
19.1.3. Spatial Audio Models 

19.2. Preliminary Material Study 

19.2.1. Game Design Documentation Study 
19.2.2. Level Design Documentation Study 
19.2.3. Complexity and Typology Evaluation to Create Audio Projects 

19.3. Sound Reference Studio 

19.3.1. Main References List by Similarity with the Project 
19.3.2. Auditory References from Other Media to Give Video Games Identity 
19.3.3. Reference Study and Drawing of Conclusions 

19.4. Sound Identity Design for Video Games 

19.4.1. Main Factors Influencing the Project 
19.4.2. Relevant Aspects in Audio Composition: Instrumentation, Tempo, etc. 
19.4.3. Voice Definition 

19.5. Soundtrack Creation 

19.5.1. Environment and Audio Lists 
19.5.2. Definition of Motif, Themes and Instrumentation 
19.5.3. Composition and Audio Testing of Functional Prototypes 

19.6. Sound Effect Creation (FX) 

19.6.1. Sound Effects: FX Types and Complete Lists According to Project Needs 
19.6.2. Definition of Motif, Themes and Creation 
19.6.3. Sound FX Evaluation and Functional Prototype Testing 

19.7. Voice Creation 

19.7.1. Voice Types and Phrase Listing 
19.7.2. Search and Evaluation of Voice Actors and Actresses 
19.7.3. Recording Evaluation and Testing of Voices on Functional Prototypes 

19.8. Audio Quality Evaluation 

19.8.1. Elaboration of Listening Sessions with the Development Team 
19.8.2. All Audio Integration into Working Prototypes 
19.8.3. Testing and Evaluation of the Results Obtained 

19.9. Project Exporting, Formatting and Importing Audio 

19.9.1. Video Game Audio Formats and Compression 
19.9.2. Exporting Audio 
19.9.3. Importing Project Audio 

19.10. Preparing Audio Libraries for Marketing 

19.10.1. Versatile Sound Library Design for Video Game Professionals 
19.10.2. Audio Selection by Type: Soundtrack, FX and Voices 
19.10.3. Commercialization of Audio Asset Libraries 

Module 20. Video Game Production and Financing 

20.1. Video Game Production 

20.1.1. Cascading Methodologies 
20.1.2. Case Studies on Lack of Project Management and Work Plan 
20.1.3. Consequences of the Lack of a Production Department in the Video Game Industry 

20.2. Development Teams 

20.2.1. Key Departments in Project Development 
20.2.2. Key Profiles in Micro-Management: LEAD and SENIOR 
20.2.3. Problems of Lack of Experience in JUNIOR Profiles 
20.2.4. Establishment of Training Plan for Low-Experience Profiles 

20.3. Agile Methodologies in Video Game Development 

20.3.1. SCRUM 
20.3.2. AGILE 
20.3.3. Hybrid Methodologies 

20.4. Effort, Time and Cost Estimates 

20.4.1. Video Game Development Costs: Main Concepts and Expenses 
20.4.2. Task Scheduling: Critical Points, Keys and Aspects to Consider 
20.4.3. Estimates based on VS Stress Points Calculated in Hours 

20.5. Prototype Planning Prioritization 

20.5.1. Establishment of General Project Objectives 
20.5.2. Prioritization of Key Functionalities and Contents: Order and Needs by Department 
20.5.3. Grouping of Functionalities and Contents in Production to Constitute Deliverables (Functional Prototypes) 

20.6. Best Practices in Video Game Production 

20.6.1. Meetings, Dailies, Weekly Meetings, End of Sprint Meetings, and ALPHA, BETA and RELEASE Milestone Review Meetings. 
20.6.2. Sprint Speed Measurement 
20.6.3. Lack of Motivation and Low Productivity Detection and Anticipation of Potential Production Problems 

20.7. Production Analysis 

20.7.1. Preliminary Analysis I: Market Status Review 
20.7.2. Preliminary Analysis 2: Establishment of Main Project References (Direct Competitors) 
20.7.3. Previous Analyses Conclusions 

20.8. Development Cost Calculation 

20.8.1. Human Resources 
20.8.2. Technology and Licensing 
20.8.3. External Development Expenses 

20.9. Investment Search 

20.9.1. Types of Investors 
20.9.2. Executive Summary 
20.9.3. Pitch Deck 
20.9.4. Publishers 
20.9.5. Self-Financing 

20.10. Project Post-Mortem Elaboration 

20.10.1. Post-Mortem Elaboration Process in the Company 
20.10.2. Positive Aspect Analysis of the Project 
20.10.3. Negative Aspect Analysis of the Project 
20.10.4. Improvement Proposal on the Project's Negative Points and Conclusions  

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"