A method for quickly modeling a meta-universe VR scene

By guiding the self-organized evolution of the field through meme primitives and dynamic intentions, and combining causal chains and historical causal correction, the problems of low efficiency, weak artistic control, and high modification costs in metaverse VR scene modeling are solved, and scene generation is achieved quickly, flexibly, and logically reasonable.

CN122156551APending Publication Date: 2026-06-05CHANGSHA CARBON CAPTURE TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202511094666.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-06
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

Existing technologies for constructing metaverse VR scenes suffer from low efficiency and reliance on professional skills in manual modeling, lack of artistic beauty and detail in procedurally generated results, high costs for modification and iteration, and a lack of internal logic and contextual relevance among scene elements.

Method used

By combining meme primitives and dynamic intent-guided fields, scene structures are generated through self-organized evolution. Causal chains and historical causal correction mechanisms are introduced to achieve rapid scene modeling and non-destructive editing.

Benefits of technology

It achieves a unity of macro-level layout and micro-level structure of the scene, improves the flexibility of design iteration and the precision of artistic control, reduces modification costs, and generates scenes with inherent logic and self-consistency.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application relates to the technical field of three-dimensional computer graphics, and discloses a meta-universe VR scene rapid modeling method, which is based on memetic primitives of geometry, connection, behavior and semantic attributes, and is macroscopically guided by a dynamic intention guide field defined by a user and containing structure, density and semantic components. Through a dynamic model based on energy minimization, the memetic primitives are driven to self-organize and evolve under the guide of the field, so that a stable connection structure is formed. The formed stable structure can generate a secondary dynamic field, which is coupled with the initial field, so that context-related emergent generation is realized. In addition, the application introduces a history trace correction mechanism based on a cause-effect chain, which allows non-destructive modification of historical design and efficient re-evolution of only the affected local part. The application deeply integrates high-level artistic guidance and bottom-layer self-organizing process, and can efficiently create a large-scale, high-quality and internally logical complex virtual scene.
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