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GTC ON-DEMAND

Presentation
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Abstract:

VR is rapidly evolving. HMD resolution and field of view are increasing, VR content is becoming more detailed, and demand for more realistic and more immersive experiences continues to grow. As we march forward in the pursuit of ever-better VR, how will we render fast enough to drive those higher resolution displays? How will we generate realistic content for enormous virtual worlds? How will we continue to enhance the quality and depth of immersion? In this panel, we'll cover topics such as human perception and neurophysiology, adaptive rendering strategies that focus compute power where it's needed, and deep learning-based synthesis for virtual models and environment. Learn how these components are being integrated to drive the future of VR.

VR is rapidly evolving. HMD resolution and field of view are increasing, VR content is becoming more detailed, and demand for more realistic and more immersive experiences continues to grow. As we march forward in the pursuit of ever-better VR, how will we render fast enough to drive those higher resolution displays? How will we generate realistic content for enormous virtual worlds? How will we continue to enhance the quality and depth of immersion? In this panel, we'll cover topics such as human perception and neurophysiology, adaptive rendering strategies that focus compute power where it's needed, and deep learning-based synthesis for virtual models and environment. Learn how these components are being integrated to drive the future of VR.

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Topics:
Virtual Reality and Augmented Reality
Type:
Panel
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9935
Streaming:
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Abstract:
Last year, we began to see promising results of applying Deep Learning in an unexpected space: hardware QA. Fast forward +365, and the efforts have been to expand on what we''ve learned, push the technology broader and into other areas that will ultimately aid in our greatest challenge: testing at scale. In this session we will highlight a new piece of the problem we are tackling: VR. We will introduce methodologies for not only addressing the unique problems that VR testing presents, but will also showcase some of the other test process areas where we are applying other Deep Learning models to gain efficiency in our overall production pipeline. From using DL on our bug mining to create a quicker path from tester to developer and back, to analysis on end user issues as a method for task automation, explore how we are enabling speed, accuracy and efficiency.
Last year, we began to see promising results of applying Deep Learning in an unexpected space: hardware QA. Fast forward +365, and the efforts have been to expand on what we''ve learned, push the technology broader and into other areas that will ultimately aid in our greatest challenge: testing at scale. In this session we will highlight a new piece of the problem we are tackling: VR. We will introduce methodologies for not only addressing the unique problems that VR testing presents, but will also showcase some of the other test process areas where we are applying other Deep Learning models to gain efficiency in our overall production pipeline. From using DL on our bug mining to create a quicker path from tester to developer and back, to analysis on end user issues as a method for task automation, explore how we are enabling speed, accuracy and efficiency.  Back
 
Topics:
AI Application Deployment and Inference, Virtual Reality and Augmented Reality, Tools and Libraries, Graphics and AI, AI for Gaming
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8262
Streaming:
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