AI Responsive Game Layout for Cross-Platform Asset Visibility
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Solution Overview
Problem
Video games designed for one platform often suffer from compromised content visibility when displayed on cross-platform devices due to differences in screen sizes and resolutions, leading to issues like misaligned elements, overlapping content, and improper scaling.
Innovation Solution
Utilizing artificial intelligence and computer vision to identify important assets in a video game and modify the content to ensure they meet visibility thresholds by adjusting scaling, field of view, and format to suit the target platform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If video frames are scaled down to fit smaller mobile device screens, then the game can be displayed on cross-platform devices, but the visibility and usability of important assets are compromised
Solution Approach 1:
The system applies different quality levels to different regions of the video frame. Important assets identified by the computer vision model are preserved at higher quality and larger size, while non-critical background areas are scaled down more aggressively. This allows the overall frame to fit smaller screens while maintaining visibility of game-critical elements.
Solution Approach 2:
The computer vision model pre-identifies important assets in the video frame before scaling occurs. By knowing which assets are critical in advance, the system can pre-arrange their placement and size in the scaled frame to ensure they meet minimum visibility thresholds, rather than attempting corrective actions after scaling.
2Area of stationary object
If the video frame is scaled down to fit mobile device screens, then cross-platform display is enabled, but the area and visibility of important assets are reduced
Solution Approach 1:
The system transitions from uniform 2D scaling to a multi-dimensional approach by considering asset importance as an additional dimension. The computer vision model assigns importance scores to different assets, allowing the system to preserve critical assets in higher resolution or larger size while reducing non-critical areas, thereby maintaining information quality across the entire frame.
3Productivity
If uniform scaling is applied to fit the mobile screen, then the entire frame is displayed, but important assets may become too small to interact with
Solution Approach 1:
Different scaling qualities are applied to different regions. Critical assets that require interaction are scaled with higher quality and larger dimensions to maintain usability, while non-interactive background elements are scaled down more aggressively. This selective approach preserves interaction capability for important game elements.
Solution Approach 2:
The system dynamically adjusts the scaling parameters based on real-time identification of important assets. Rather than applying a fixed uniform scale, the scaling factors are dynamically modified to ensure interactive assets meet minimum size thresholds for usability on mobile devices.
Data Source
AI summary
A method including executing a video game to generate a video frame for presentation on a device of a first platform for a game play by a user. The method including determining a target device of a second platform. The method including mapping the video frame to the target device. The method including determining an area of focus in a scene in the video frame based on a game context. The method including classifying an asset in the area of focus using a computer vision model implementing artificial intelligence. The method including determining that the asset is important in the game play using the computer vision model. The method including determining that the asset in the video frame that is mapped does not meet a threshold of visibility. The method including modifying the video frame that is mapped so that the asset meets the threshold of visibility.