Neural Network Rendering Simulation for Realistic Image Generation
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Solution Overview
Problem
Current methods for generating realistic 3D rendered images are costly and time-consuming, requiring high-quality 3D shape data, high-resolution mesh, and high-capacity computation, and struggle to achieve real-time rendering of human body images without cognitive discomfort.
Innovation Solution
A method using a neural network to generate a rendering simulation image from real image data, separating foreground and background to extract latent and background feature information, and then producing a realistic image through a content map and style map, leveraging a pretrained neural network for efficient image generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-quality 3D shape data, high-resolution mesh, and large-scale high-speed computation are used to generate realistic rendered images, then the realism and quality of the rendered images are improved, but the time consumption, cost, and computational resources increase significantly
Solution Approach 1:
The patent pre-trains a neural network model using high-quality real images and rendering simulation images before actual rendering. This preliminary training phase allows the model to learn realistic image characteristics in advance, enabling fast real-time rendering without requiring high computational resources during the actual rendering process. The pre-trained model can generate realistic images quickly while maintaining high quality.
Solution Approach 2:
The patent creates rendering simulation images that copy the appearance and characteristics of real images. By training the neural network to map between rendering simulation images and real images, the system learns to generate realistic images from 3D data without requiring expensive real-time rendering calculations. The simulation images serve as intermediaries that capture the essential visual properties needed for realistic rendering.
2Productivity
If high-capacity computation and high-speed 3D graphic rendering hardware are deployed to achieve real-time rendering, then the rendering speed is improved, but the cost and power consumption increase
Solution Approach 1:
The patent replaces traditional mechanical 3D rendering systems with a neural network-based system. Instead of relying on complex rendering hardware and algorithms to generate images in real-time, the system uses a pre-trained neural network that has already learned the mapping from 3D data to realistic images. This substitution dramatically reduces the computational burden and hardware requirements during actual rendering operations.
Solution Approach 2:
The computationally intensive work is performed in advance during the pre-training phase, where the neural network learns from large datasets of real and simulation images. Once trained, the model can perform rapid inference without requiring high-capacity computation during real-time rendering, thus reducing hardware complexity and power consumption.
3Manufacturing precision
If traditional rendering methods are used to generate human body images, then the images can be produced, but they fail to overcome cognitive discomfort and achieve true realism
Solution Approach 1:
The patent employs a feedback mechanism during the pre-training phase where the neural network learns to map rendering simulation images to real images. The model receives feedback from the training data, adjusting its parameters to minimize the difference between generated and actual real images. This feedback-driven learning enables the system to capture subtle realistic characteristics that traditional rendering methods miss, achieving cognitive realism.
Solution Approach 2:
The patent transforms the rendering process by changing the fundamental parameters and approach. Instead of using traditional rendering algorithms with fixed parameters, the system uses a neural network with learnable parameters that are optimized during pre-training. This allows the model to adapt to the specific characteristics of real images and generate outputs that are cognitively realistic, overcoming the limitations of conventional methods.
Data Source
AI summary
Provided is a rendering method and a device for improving realism of a rendered image. The method includes receiving training image data including a real image, generating a rendering simulation image using the training image data, acquiring background feature information by separating foreground and background areas on the basis of the rendering simulation image or the training image data, acquiring latent feature information required for generating a realistic image on the basis of the rendering simulation image, and generating a realistic image on the basis of the latent feature information and the background feature information.