3D Scene Image Generation with Scene Encoding for Realistic Views
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Solution Overview
Problem
Existing solutions for generating 2D images of 3D scenes do not adequately account for the 3D environment, leading to inaccurate, non-immersive content due to neglecting factors like perspective, occlusion, and lighting.
Innovation Solution
A computer-implemented method using a scene encoder and a generative image model trained on a dataset of 2D images and corresponding 3D scene layouts and viewpoints, incorporating a diffusion model with cross-attention to generate realistic 2D images from high-level scene descriptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If generative deep learning models are used to generate 2D images of 3D scenes, then image generation capability is improved, but accuracy and realism deteriorate due to neglecting 3D environmental factors
Solution Approach 1:
The patent introduces a scene encoder as an intermediary component that processes 3D scene information (layout, viewpoint, object positions) and transforms it into a scene encoding tensor. This tensor serves as a mediator that bridges the gap between 3D scene data and the generative image model, enabling the model to understand and incorporate 3D environmental factors like perspective, occlusion, and lighting relationships without requiring direct complex 3D processing in the generation stage.
Solution Approach 2:
The patent transforms the 3D scene representation by changing parameters - converting spatial coordinates, object layouts, and viewpoint information into a standardized scene encoding tensor format. This parameter transformation allows the generative model to work with condensed 3D information efficiently while maintaining accuracy in representing 3D relationships in the generated 2D images.
2Manufacturing precision
If comprehensive 3D environmental factors are incorporated into image generation, then image realism is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex task of generating realistic 2D images from 3D scenes into distinct functional modules: a scene encoder that processes 3D scene data (layout, viewpoint, objects) separately, and a generative image model that produces the final image. This segmentation allows each component to specialize in specific aspects of the task, managing complexity by dividing the overall system into manageable, independent parts that can be developed and optimized separately.
Solution Approach 2:
The patent extracts essential 3D environmental factors (layout, viewpoint, object positions) from the complete 3D scene data and concentrates them into a scene encoding tensor. This extraction process isolates the critical information needed for realistic image generation while discarding redundant details, thereby reducing system complexity while maintaining image realism.
3Manufacturing precision
If large pre-trained models and external modules are used to generate high-fidelity images, then image quality is improved, but computational resources and time increase
Solution Approach 1:
The patent performs preliminary action by pre-processing 3D scene information through the scene encoder to create a compact scene encoding tensor before the image generation stage. This preliminary encoding condenses complex 3D data into an efficient representation that the generative model can process quickly, avoiding the need for large pre-trained models to handle raw 3D data directly, thus improving generation speed while maintaining fidelity.
Solution Approach 2:
The patent transitions from working with high-dimensional raw 3D scene data to a condensed scene encoding tensor representation, effectively changing the dimensionality and structure of the input data. This dimensional transformation enables the generative model to process scene information more efficiently, achieving high-fidelity image generation without requiring excessive computational resources or time.
Data Source
AI summary
A computer-implemented method for machine-learning a function that generates a 2D image of a 3D scene. The function includes a scene encoder and a generative image model. The scene encoder takes as input a layout of the 3D scene and a viewpoint and outputs a scene encoding tensor. The generative image model takes as input the scene encoding tensor outputted by the scene encoder and outputs the generated 2D image. The machine-learning method includes obtaining a dataset comprising 2D images and corresponding layouts and viewpoints of 3D scenes. The machine-learning method includes training the function based on the obtained dataset. Such a machine-learning method forms an improved solution for generating a 2D image of a 3D scene.