Deep Neural Network HUD Removal for Immersive Video
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Solution Overview
Problem
Existing applications, such as video games, often lack options for customizing or removing heads-up display (HUD) elements, leading to a less immersive experience and reduced replayability.
Innovation Solution
The use of deep neural networks (DNNs) to identify and remove HUD elements from frames, allowing for the generation and reconstruction of frames without these elements, thereby providing HUD customization options not originally available in the application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If HUD elements are displayed to provide supplemental information, then user information access is improved, but user immersion and experience quality deteriorate
Solution Approach 1:
The system dynamically removes or obscures HUD elements based on detected spectator presence. During normal gameplay, HUD elements are displayed to provide information. When spectator detection occurs, the system dynamically adjusts by removing these elements to enhance immersion for viewers, thereby resolving the contradiction between information access and user immersion based on contextual conditions.
2Adaptability or versatility
If HUD customization options are provided, then user experience and replayability are improved, but device complexity and development effort increase
Solution Approach 1:
The system provides HUD customization functionality without requiring developer implementation. The spectator detection system automatically determines when and how to remove HUD elements, eliminating the need for developers to build complex customization interfaces. This self-service approach allows the system to adapt to spectator needs while avoiding the development complexity of traditional customization solutions.
Solution Approach 2:
The spectator detection system serves multiple functions: it detects spectator presence, automatically controls HUD visibility, and enhances replayability. This multi-functional approach consolidates what would otherwise require separate customization features into a single universal system, reducing overall device complexity while maintaining adaptability.
3Reliability
If standard HUD elements are displayed, then application functionality is maintained, but spectator experience and re-watchability deteriorate
Solution Approach 1:
The system dynamically adjusts HUD visibility based on spectator detection. During normal gameplay, standard HUD elements are displayed to maintain application functionality. When spectators are detected, the system dynamically removes or obscures these elements to enhance spectator experience and re-watchability, thereby resolving the contradiction between maintaining functionality and improving spectator experience based on contextual conditions.
Data Source
AI summary
Approaches presented herein provide systems and methods for identifying and removing overlay elements from one or more frames in a video sequence. A frame may be evaluated to identify one or more overlay elements and a mask may be generated, or acquired, to identify one or more regions of the frame associated with the one or more overlay elements. The initial frame and mask may be used as an input to one or more neural networks to remove the overlay elements and replace the overlay elements with generated content associated with an underlying scene in the frame. A reconstructed frame may then be generated and inserted into the video sequence, replacing the frame, for view on a display.