Neural Style Transfer for Real-Time Volumetric Image Effects
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Solution Overview
Problem
Rendering volumetric effects such as fog, smoke, and fire in computer graphics is computationally expensive and often results in low quality due to high processing demands, especially in interactive applications like video games.
Innovation Solution
Utilize a neural style transfer (NST) model to post-process fog-free content images using a trained artificial neural network (ANN) to generate images with volumetric effects, reducing the need for complex volumetric rendering operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional volumetric rendering operations are used to render fog, smoke, and other volumetric effects, then the visual realism and quality of the rendered scene is improved, but the computational cost and processing time increase significantly
Solution Approach 1:
The patent replaces traditional mechanical volumetric rendering operations with a neural network-based system. The neural network is trained offline to learn the mapping from input scene parameters to volumetric effect outputs, and during runtime, it rapidly generates volumetric effects by processing scene data through the trained network, substituting computationally intensive rendering calculations with efficient neural network inference
Solution Approach 2:
The patent performs preliminary training of the neural network offline before actual rendering. During this preliminary phase, the network learns from extensive training data to capture complex volumetric effects. At runtime, only lightweight inference is needed, having already performed the heavy computational work of learning the rendering transformations in advance
2Manufacturing precision
If complex volumetric rendering operations are performed to achieve high-quality volumetric effects, then the rendering quality is improved, but the device complexity and processing requirements increase
Solution Approach 1:
The patent substitutes complex volumetric rendering algorithms with a pre-trained neural network model. The neural network encapsulates complex rendering knowledge learned during training, allowing the system to achieve high rendering quality without implementing complex volumetric rendering pipelines, ray marching, or particle simulation systems
Solution Approach 2:
The patent uses a neural network that has been trained to copy or replicate the appearance of complex volumetric effects. Instead of computing these effects from first principles during rendering, the system uses the trained network to generate visually similar results through learned patterns, effectively copying the appearance of complex volumetric rendering with simpler operations
3Reliability
If traditional volumetric rendering is used in interactive applications like video games, then the volumetric effects can be rendered, but the processing time and computational resources required become burdensome for real-time interaction
Solution Approach 1:
The patent performs the computationally intensive learning phase offline before interactive applications run. The neural network is trained on extensive volumetric rendering data in advance, capturing the relationships between scene parameters and volumetric effect appearances. During interactive applications, only fast inference is required, enabling real-time performance
Solution Approach 2:
The patent replaces time-consuming volumetric rendering calculations with efficient neural network inference. The neural network processes scene inputs through optimized computation graphs that are much faster than traditional volumetric rendering algorithms, enabling real-time interactive applications to achieve reliable volumetric effects without excessive processing time
Data Source
AI summary
A data processing apparatus rendering circuitry to render content images for a virtual environment, and image processing circuitry to generate one or more output images in response to one or more of the content images, wherein the image processing circuitry is configured to input at least one 2D volumetric effect image and one or more of the content images to a neural style transfer “NST” model, the NST model being trained to generate one or more of the output images using the at least one 2D volumetric effect image as a style image.