Unified Image and Depth Map Generation Model
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Solution Overview
Problem
Conventional image generation systems using machine learning struggle to efficiently generate images and depth maps that are spatially aligned and photorealistic, often requiring separate models for image generation and inpainting, which is costly and time-consuming.
Innovation Solution
An image generation system that embeds a prompt to obtain guidance features and uses a single image generation machine learning model to simultaneously generate an image and a depth map, eliminating the need for separate depth map generation algorithms and allowing for higher user control over the content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate models are used for image generation and depth map generation, then the functionality is more specialized, but the system complexity and processing time increase
Solution Approach 1:
The patent combines image generation and depth map generation into a single unified model. The model simultaneously outputs both the color image and depth map from the same input prompt, eliminating the need for separate specialized models and reducing system complexity while maintaining both functionalities.
Solution Approach 2:
The unified model serves multiple functions by generating both color images and depth maps from a single prompt input. This multi-functional approach allows the system to perform both image synthesis and depth estimation tasks without requiring separate specialized models.
2Manufacturing precision
If separate models are used for image generation and depth map generation, then each model can be optimized for its specific task, but the processing time and computational cost increase
Solution Approach 1:
The patent merges image generation and depth map generation into a single simultaneous process. The unified model generates both outputs in one forward pass, eliminating the sequential processing time required when using separate models while maintaining task-specific optimization through shared latent representations.
3Reliability
If separate models are used for image generation and inpainting, then the specialized functionality is improved, but the expense and time consumption increase
Solution Approach 1:
The unified model provides multi-functional capability by handling both image generation and inpainting tasks. The model can generate images from prompts and perform inpainting on existing images, maintaining specialized functionality for each task while improving overall system efficiency through a single versatile model.
Data Source
AI summary
Methods, non-transitory computer readable media, apparatuses, and systems for image and depth map generation include receiving a prompt and encoding the prompt to obtain a guidance embedding. A machine learning model then generates an image and a depth map corresponding to the image based on the guidance embedding. The image and the depth map are each generated based on the guidance embedding.