3D Scene Image Generation With Conditioning and Viewpoint Control
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Solution Overview
Problem
Existing solutions for generating 2D images from 3D scenes do not adequately account for the 3D environment, including perspective, occlusion, and lighting factors, and lack control over the visual appearance of the generated images.
Innovation Solution
A computer-implemented method using a machine-learning function with a scene encoder and generative image model to generate 2D images from 3D scenes, incorporating conditioning signals to control the visual aspect and account for 3D structures and relationships, trained end-to-end with a dataset including user-defined layout and viewpoint data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If generative deep learning models are used to generate 2D images from 3D scenes, then image generation capability is improved, but accuracy and realism are worsened due to inability to account for 3D environment factors
Solution Approach 1:
The patent introduces a scene encoder as an intermediary component that processes 3D scene data (including perspective, occlusion, and lighting information) and transforms it into a scene encoding tensor. This tensor then conditions the generative image model, allowing the model to generate images that accurately reflect 3D environmental factors without requiring the model itself to directly process complex 3D geometry.
Solution Approach 2:
The system segments the image generation process into distinct functional components: a scene encoder that handles 3D environmental analysis and a generative image model that handles visual synthesis. This segmentation allows each component to specialize in its respective task, improving overall accuracy while maintaining automation.
2Adaptability or versatility
If control over visual appearance is added to the generation process, then versatility is improved, but system complexity is worsened
Solution Approach 1:
The scene encoder serves multiple functions: it processes 3D scene geometry, analyzes spatial relationships, extracts environmental features (lighting, occlusion, perspective), and outputs a condensed scene encoding tensor. This multi-functionality allows comprehensive visual control without proportionally increasing system complexity.
Solution Approach 2:
The system controls visual appearance by adjusting parameters in the scene encoding tensor, which is derived from 3D scene data. By modifying these encoded parameters (such as lighting conditions, camera perspective, or object arrangements in the 3D scene), users can control the visual appearance of generated images without directly manipulating the complex generative model architecture.
3Reliability
If perspective, occlusion and lighting factors are incorporated, then realism is improved, but computational requirements are worsened
Solution Approach 1:
The scene encoder performs preliminary processing of 3D scene data before the generative model creates images. It pre-computes scene encoding tensors that capture perspective, occlusion, and lighting information in advance, allowing the generative model to work with pre-processed, condensed information rather than raw 3D data, thus reducing computational energy during image generation.
Solution Approach 2:
The scene encoder extracts only the essential 3D environmental features (perspective, occlusion, lighting) from the complete 3D scene data and represents them in a condensed scene encoding tensor. This extraction process removes unnecessary computational overhead while retaining the critical factors needed for realistic image generation.
Data Source
AI summary
A computer-implemented method for generating a 2D image of a 3D scene. The method comprises obtaining arrangement data comprising a layout of the 3D scene and at least one conditioning signal. Each conditioning signal has a type among a predetermined set of at least two types. The method comprises applying a machine-learning function to the obtained arrangement data and viewpoint. The function comprises a scene encoder and a generative image model. The scene encoder takes as input the obtained arrangement data and viewpoint and outputting a scene encoding tensor. The generative image model takes as input the scene encoding tensor outputted by the scene encoder and outputting the generated 2D image. Such a generating method forms an improved solution for controllably generating a 2D image of a 3D scene.