3D Model Generation With Hierarchical Image Prompt Control
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Solution Overview
Problem
Existing machine learning models struggle with generating accurate and detailed 3D models from images due to the complexity of visual information, leading to blurry or incomplete reconstructions with inconsistent textures and geometry.
Innovation Solution
A multi-level image-prompt controller comprising a global controller, local controller, and pixel controller is employed to guide the diffusion model, along with canonical camera coordination and background alignment, to enhance geometric accuracy and texture detail in 3D model generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a diffusion model is used to generate 3D models from images, then the ability to handle complex visual information is improved, but the geometric accuracy and texture detail deteriorate due to blurry or incomplete reconstructions
Solution Approach 1:
The system segments the 3D generation task into multiple components: a diffusion model for handling complex visual information and generating diverse 3D structures, and a neural radiance field (NeRF) model for ensuring geometric accuracy and detailed textures. This segmentation allows each model to specialize in its strength while compensating for the other's weaknesses.
Solution Approach 2:
The patent creates a composite modeling approach by combining two different 3D representation methods (diffusion models and neural radiance fields) into a unified system. The diffusion model generates initial 3D structures while the NeRF model refines geometric accuracy and texture details, producing a composite output that leverages the advantages of both approaches.
2Productivity
If machine learning models generate 3D models from images, then content generation capability is improved, but the reconstruction quality worsens due to inconsistent textures and geometry
Solution Approach 1:
The system implements a feedback mechanism where the diffusion model generates initial 3D models which are then evaluated and refined by the neural radiance field model. The NeRF model provides feedback on geometric accuracy and texture consistency, iteratively improving the reconstruction quality while maintaining high content generation productivity.
Solution Approach 2:
The patent employs parameter changes by adjusting the complexity and resolution parameters during the generation process. The diffusion model operates with parameters optimized for creative generation, while the NeRF refinement stage uses parameters tuned for geometric precision and texture fidelity, dynamically changing parameters to balance productivity and reliability.
3Device complexity
If a single model is used for 3D generation, then the system complexity is reduced, but the generation quality worsens due to inability to address multiple challenges simultaneously
Solution Approach 1:
The system is segmented into two specialized models: a diffusion model for handling complex visual information and generating diverse 3D content, and a neural radiance field model for ensuring geometric accuracy and texture detail. This segmentation increases system complexity but resolves the contradiction by allowing each component to optimize for its specific function.
Solution Approach 2:
The patent creates a multi-functional system where the diffusion model handles creative generation and the NeRF model handles geometric refinement. Together, they form a universal 3D generation system that can simultaneously address multiple challenges (visual complexity, geometric accuracy, texture detail) that a single model cannot handle alone.
Data Source
AI summary
The present disclosure describes techniques for generating three-dimensional models using machine learning models. A two-dimensional (2D) image is input into a machine learning model. The machine learning model is configured to generate three-dimensional (3D) models with accurate geometry and detailed textures. A set of multi-view images is generated based at least in part on the 2D image by a first sub-model of the machine learning model. The first sub-model comprises a multi-level image prompt controller configured to implement hierarchical controls over generating multi-view images by the first sub-model based at least in part on an input image. A 3D model is generated based at least in part on the set of multi-view images by a second sub-model of the machine learning model. The second sub-model is configured to implement a background alignment and a camera alignment for improving quality and geometric accuracy of the generated 3D models.