Semantic-Feature Neural Rendering for Realistic Block-Based Worlds

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Solution Overview

Problem

Existing virtual world creation applications, such as Minecraft and Roblox, produce environments that appear blocky and cartoonish, lacking realism, which limits their appeal to certain users.

Innovation Solution

A system that utilizes neural networks to generate photorealistic images from user-created block-based virtual environments by extracting semantic features, projecting them into a 2D representation, and using generative models like GANs to render realistic scenes based on these features.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If block-based virtual environment creation tools are used, then ease of operation is improved, but manufacturing precision deteriorates

Engineering Contradiction:

Improveease of operationVSAvoidvisual realism

Core Design Contradiction:

Ease of operationVSManufacturing precision

Solution Approach 1:

The patent introduces neural networks as an intermediary between the simple block-based environment definition and the final photorealistic image generation. The neural network translates the coarse block structure into detailed realistic imagery, allowing users to maintain simple block-based operations while achieving high visual realism through the intermediary processing of the neural network model

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces the traditional mechanical rendering system that directly displays block structures with a neural network-based generative system. Instead of using conventional graphics rendering to display blocks, the system uses trained neural networks to generate photorealistic images from block-based semantic definitions, substituting the mechanical rendering approach with an AI-driven generation approach

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Manufacturing precision

If photorealistic image generation is implemented, then manufacturing precision is improved, but device complexity increases

Engineering Contradiction:

Improvevisual realismVSAvoidsystem complexity

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The patent applies preliminary action by pre-training neural networks on large datasets of real-world images before deployment. The neural network is trained in advance to understand the mapping between block-based semantic definitions and photorealistic imagery, so that during actual use, the complex training process is avoided and only inference is required, reducing the operational complexity while maintaining high visual realism

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent uses copying by training the neural network to learn from copies of real-world images. The model is trained on datasets containing photographs of real environments, learning to generate realistic imagery by copying patterns and features from these training images. This allows the system to produce photorealistic output without requiring complex real-time processing, as the knowledge has been pre-copied into the neural network weights

Inventive Principle:

Principle #26Copying

Data Source

PatentUS12387430B2Generating images of virtual environments using one or more neural networks

Publication Date: 2025.08.12 NVIDIA CORP

AI summary

Apparatuses, systems, and techniques are presented to generate images. In at least one embodiment, one or more neural networks are used to generate one or more images based, at least in part, upon one or more semantic features projected from a three-dimensional environment.