Neural Image Generation With 3D Mesh Lighting Rendering
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Solution Overview
Problem
Existing techniques for generating image content, especially in complex lighting conditions, require significant computing resources and are inefficient.
Innovation Solution
Utilizing neural networks, specifically neural fields, to encode 3D representations of scenes, enabling efficient rendering of 2D images with lighting effects by combining primary and secondary ray tracing, including volumetric rendering and physics-based bidirectional reflectance distribution functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If traditional techniques are used to generate image content with complex lighting conditions, then image quality with lighting effects is maintained, but computing resource consumption increases significantly
Solution Approach 1:
The patent segments the image generation process into two distinct stages: (1) a neural network stage that generates base image content, and (2) a rendering stage that applies lighting effects using ray tracing techniques. This segmentation allows each stage to be optimized independently, reducing overall computational resource consumption while maintaining image quality.
Solution Approach 2:
The patent applies preliminary action by pre-computing and storing lighting parameters and scene geometry data before the actual rendering process. The neural network generates base images without full lighting effects, and pre-computed lighting data is later applied during rendering, avoiding the need to compute complex lighting in real-time for every image generation task.
2Productivity
If neural networks are used to generate 2D images, then generation speed is improved, but lighting effects quality deteriorates without proper rendering techniques
Solution Approach 1:
The patent merges two different approaches: neural network-based image generation (which provides speed) and traditional ray tracing rendering (which provides lighting quality). The system combines the generated 2D images with 3D mesh data and applies physics-based rendering to add realistic lighting effects, achieving both speed and quality.
Solution Approach 2:
The patent introduces an intermediary rendering process that acts as a bridge between the neural network-generated 2D images and the final output. This intermediary stage applies lighting effects, shadows, and material properties using pre-computed 3D scene data, transforming the basic neural network output into a photorealistic image without requiring the neural network itself to compute complex lighting.
3Measurement precision
If full physics-based rendering is applied to all image generation tasks, then lighting accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies local quality by using physics-based rendering selectively only for lighting effects and material appearances, while relying on the neural network for base image content generation. This localized application of complex rendering techniques reduces overall system complexity while maintaining lighting accuracy where it matters most.
Solution Approach 2:
The patent performs preliminary computation of lighting parameters, scene geometry, and material properties before the rendering stage. By pre-computing these complex physics-based parameters in advance, the system reduces the computational burden during actual image generation while maintaining high lighting accuracy.
Data Source
AI summary
Apparatuses, systems, and techniques to generate a a two-dimensional (2D) image. In at least one embodiment, one or more neural networks are used to generate the image and a three-dimensional (3D) mesh representation of the image, wherein the 3D mesh representation is to be used to apply one or more effects to the 2D image.