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1
Introduction
2
Overview
3
Goals
4
Challenges
5
Outline
6
Understanding Humans
7
Full Body Posture
8
Goal
9
Example
10
More examples
11
Gaming applications
12
YouTube videos
13
Hands
14
Objects
15
Reconstructing Objects
16
Neural Network Architecture
17
Mesh parametric mesh reconstruction
18
Car reconstruction
19
Microsoft Flight Simulator
20
The State of Computer Vision
21
Computer Vision vs Computer Graphics
22
Hand Pose Estimation
23
Hand Pose Estimation Example
24
Virtual Cars
25
ContextAware Mixed Reality
26
Synthetic Data
27
Gaming Pipeline
28
Conclusion
29
Summary
30
Thank you
Description:
Explore computer vision and deep learning applications in gaming through this 29-minute webinar featuring Srinath Sridhar. Delve into topics such as full body posture estimation, hand pose detection, object reconstruction, and neural network architectures for 3D modeling. Discover how these technologies are revolutionizing gaming experiences, from Microsoft Flight Simulator to context-aware mixed reality. Gain insights into the challenges and goals of implementing computer vision in gaming, and understand the differences between computer vision and computer graphics. Learn about synthetic data generation and its role in advancing gaming pipelines. Conclude with a comprehensive summary of the state of computer vision in gaming and its future potential.

3D Deep Learning for Gaming with Srinath Sridhar and Stanford Artificial Intelligence

Resemble AI
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