3D Scene Image Generation With Conditioning Signal Control
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Solution Overview
Problem
Existing solutions for generating 2D images from 3D scenes do not adequately utilize 3D environment information, such as perspective, occlusion, and lighting, 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 scene arrangement data and conditioning signals, allowing control over the generated images.
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 object positions, orientations, and environmental factors) and transforms it into a scene encoding tensor. This tensor serves as a bridge between the 3D scene representation and the generative image model, enabling the model to accurately account for perspective, occlusion, and lighting effects while maintaining automated image generation.
2Extent of automation
If generative deep learning models are used for 2D image generation, then automation is improved, but control over visual appearance is worsened
Solution Approach 1:
The patent enables different levels of control over visual appearance by allowing users to specify conditioning signals for different components of the 3D scene. Users can control overall scene characteristics (lighting, atmosphere) while also controlling specific object properties (color, material, shape). This localized control approach maintains automation while providing versatile adaptability over various visual aspects.
3Productivity
If current generative solutions are applied, then image generation speed is improved, but realism and immersion are worsened due to lack of 3D environmental awareness
Solution Approach 1:
The patent performs preliminary processing of 3D scene data through the scene encoder, which extracts and encodes relevant environmental information (object positions, orientations, lighting conditions, occlusion relationships) into a scene encoding tensor before passing it to the generative model. This preliminary action enables the generative model to quickly produce realistic images by leveraging pre-processed 3D environmental awareness, maintaining high generation speed while improving realism.
Data Source
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AI summary
The disclosure notably relates to 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.