Interactive Image Generation Using Synthetic Depth Maps
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Solution Overview
Problem
Existing techniques for generating three-dimensional models or specifications for interactive images are resource-intensive and prohibitive for many applications, making efficient interactive image generation difficult.
Innovation Solution
A content generation platform that combines actually captured content with computer-generated content using neural networks, allowing for the creation of interactive imagery with partial three-dimensional capabilities through a process involving a backend stage for image capture, an image processing stage using machine learning, and a front end stage for user interaction and customization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If three-dimensional models or specifications are generated using existing techniques, then interactive features can be provided with respect to the image, but the process becomes resource-intensive and prohibitive for many applications
Solution Approach 1:
The patent creates a synthetic depth map that copies the essential three-dimensional information needed for interactive features without requiring a complete three-dimensional model. This synthetic depth map serves as a simplified representation that enables depth-aware operations while significantly reducing computational resources compared to full 3D modeling approaches.
Solution Approach 2:
Instead of generating complete three-dimensional models with all possible details, the patent applies partial action by creating only the depth information necessary for interactive features. This selective approach generates just enough three-dimensional data to enable functionality like depth-based editing, object selection, and spatial relationships without the overhead of comprehensive 3D reconstruction.
2Adaptability or versatility
If complete three-dimensional models are generated to enable interactive features, then interactivity is achieved, but the complexity and resource requirements become prohibitive
Solution Approach 1:
The patent synthesizes a depth map that copies only the essential geometric information needed for interactive operations. This synthetic depth representation captures spatial relationships and depth cues necessary for interactivity while avoiding the complexity of complete three-dimensional modeling, including surface geometry, material properties, and full spatial reconstruction.
Solution Approach 2:
The patent segments the three-dimensional information into only the depth components necessary for interactive features. Rather than processing complete three-dimensional models with all their complexity, the system extracts and processes only depth map data, separating the essential interactive functionality from the unnecessary complexity of full 3D model generation.
3Manufacturing precision
If traditional methods are used to capture extensive data for three-dimensional modeling, then complete models can be created, but the time and resources required become prohibitive for efficient image generation
Solution Approach 1:
The patent creates a synthetic depth map that copies depth information from the input image using machine learning algorithms. This approach achieves sufficient depth accuracy for interactive features without requiring extensive multi-view captures, laser scanning, or other time-consuming data collection methods associated with traditional three-dimensional modeling.
Solution Approach 2:
The patent replaces mechanical data capture systems (such as multi-camera rigs, laser scanners, or structured light projectors) with a machine learning-based synthetic depth map generation system. This substitution uses computational algorithms to infer depth information directly from standard images, dramatically reducing the time and equipment needed while maintaining sufficient accuracy for interactive operations.
Data Source
AI summary
A content generation platform is generally described herein. More specifically, interactive image generation and techniques and features thereof are disclosed herein. One or more sets of images of a scene are captured in an imaging studio. The captured one or more sets of images of the scene are processed using one or more machine learning based networks to generate an interactive image of the scene comprising a plurality of interactive features. One or more of the plurality of interactive features of the generated interactive image may be modified or edited according to user preferences.