Virtual Space Model Generation Control for Data Load Reduction
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Solution Overview
Problem
Current systems for remote communication in virtual spaces lack efficient methods to dynamically adjust data processing and transmission loads, affecting communication quality.
Innovation Solution
An information processing apparatus and method that acquires an importance level for parts of objects in a virtual space, controlling the generation of models based on this level to optimize data amounts, reducing transmission and processing loads while maintaining high accuracy for critical parts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution models of all objects are generated and transmitted, then model accuracy is improved, but data transmission load and processing calculation load increase
Solution Approach 1:
The patent applies local quality by differentiating model generation quality based on spatial location and importance. Critical regions (e.g., character models, interactive objects) are rendered with high resolution and detailed data, while non-critical regions (e.g., background scenery, distant objects) use lower resolution and simplified data representations. This selective quality assignment reduces overall data transmission load while maintaining accuracy where it matters most for user experience.
Solution Approach 2:
The virtual space is segmented into multiple regions with different importance levels. The system divides the scene into foreground/background, interactive/non-interactive, and near/far zones, applying different model generation strategies to each segment. This segmentation allows the system to optimize data transmission by sending high-detail models only for segmented regions that require them, rather than transmitting uniformly high-resolution data for the entire virtual space.
2Reliability
If detailed models of all objects are generated, then communication quality is improved, but processing calculation load increases
Solution Approach 1:
The system dynamically adjusts model generation parameters based on real-time conditions such as user position, interaction state, and network bandwidth availability. When a user approaches or interacts with an object, the system dynamically increases model detail for that specific object. When the user moves away or network conditions deteriorate, the system dynamically reduces model complexity. This dynamic adaptation maintains communication quality during critical interactions while reducing processing load during transitions or low-priority scenarios.
Solution Approach 2:
The patent changes key parameters of model generation including resolution, polygon count, texture detail, and animation complexity based on importance levels. For high-importance objects, parameters are set to maximum quality; for low-importance objects, parameters are reduced. This parameter adjustment strategy ensures that processing resources are concentrated on generating detailed models only where necessary for effective communication, rather than uniformly processing all objects at high quality.
3Measurement precision
If uniform high-quality model generation is applied to all parts, then overall accuracy is improved, but data transmission efficiency decreases
Solution Approach 1:
The system implements local quality by assigning different accuracy levels to different parts of the virtual space based on their importance. Critical components such as character models, facial expressions, and interactive objects receive high-accuracy treatment with detailed geometry and textures. Non-critical components like background environments, static props, and distant objects use lower-accuracy representations. This localized quality differentiation maintains overall communication effectiveness while significantly improving data transmission efficiency compared to uniform high-quality generation.
Data Source
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AI summary
An information processing apparatus according to an embodiment of the present technology includes: an acquisition unit; and a generation control unit. The acquisition unit acquires an importance level relating to at least one part of an object. The generation control unit controls, on the basis of the acquired importance level, generation of a model of the object displayed in a virtual space.