3D Scene Image Generation With Cross-View Latent Consistency
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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, and inconsistent images across different viewpoints.
Innovation Solution
A computer-implemented method using a machine-learning model with a scene encoder and generative image model to generate multiple 2D images, considering the 3D scene layout, viewpoint, and latent vectors, with latent vector combinations to ensure consistency across views.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generative deep learning models are used to generate 2D images of 3D scenes, then image generation capability is improved, but accuracy and immersion are worsened due to lack of 3D environment consideration
Solution Approach 1:
The patent introduces a mediator mechanism that bridges 3D scene data and 2D image generation. A 3D-aware diffusion model serves as an intermediary, processing both 3D scene representations and 2D image targets to generate consistent multi-view images. This mediator enables the system to leverage 3D spatial information while maintaining 2D image quality and consistency.
Solution Approach 2:
The patent implements feedback mechanisms through iterative optimization processes. The system generates initial 2D images, compares them with target images, and uses the differences to refine subsequent generations. This feedback loop continues until convergence, ensuring high accuracy while maintaining generation capability.
2Reliability
If 3D scene knowledge is integrated into image generation, then visual consistency is improved, but computational complexity increases
Solution Approach 1:
The patent segments the computational task into distinct modules: 3D scene representation module, image generation module, and consistency optimization module. Each module handles specific computations independently, reducing overall complexity while maintaining visual consistency through coordinated operation.
Solution Approach 2:
The patent transforms 3D scene information into 2D latent representations that can be processed by diffusion models. By projecting 3D data into a compressed latent space, the system reduces computational dimensionality while preserving essential spatial relationships for consistent image generation.
3Quantity of substance
If multiple 2D images are generated from a single 3D scene, then content richness is improved, but consistency across viewpoints deteriorates
Solution Approach 1:
The patent creates a universal 3D-aware diffusion model that can generate multiple 2D images from a single 3D scene while maintaining consistency. The model is trained to handle various viewpoints and generates images that are both diverse in content and consistent in their 3D representation, achieving multi-functionality in a single system.
Solution Approach 2:
The patent changes parameters such as latent vector embeddings and diffusion steps to control viewpoint consistency. By adjusting these parameters during generation, the system produces multiple consistent images that accurately represent the 3D scene from different perspectives without compromising visual coherence.
Data Source
AI summary
A computer-implemented method for generating a plurality of 2D images of a 3D scene. The method comprises obtaining arrangement data and a machine-learning model configured for generating a 2D image. The method comprises generating a plurality of first 2D images of the 3D scene each having a respective viewpoint. The method comprises, for each generated first 2D image, computing a first latent vector. The method comprises, for each generated first 2D image, computing a second latent vector as a weighted combination of the computed first latent vectors. The method comprises generating a plurality of second 2D images of the 3D scene by applying, for each given viewpoint of the first 2D images, the model to the computed second latent vector. Such a generating method forms an improved solution for generating a plurality of 2D images of a given 3D scene.