Semantically-Aware Image Extrapolation for Realistic Scene Expansion
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Solution Overview
Problem
Existing image extrapolation techniques either require significant manual effort to add diverse and realistic objects or backgrounds or produce images lacking saliency and realism, limiting their applicability to complex subjects.
Innovation Solution
A computer-implemented method using machine-learning-based object generation and instance-aware normalization to automatically extrapolate images, incorporating unique objects and features that blend seamlessly with the original image, respecting semantic continuity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated texture replication methods are used for image extrapolation, then processing efficiency is improved, but the saliency and realism of added objects deteriorate
Solution Approach 1:
The system uses instance-aware context normalizers that automatically adapt to each generated instance, allowing the extrapolation process to self-regulate and maintain realism without manual intervention. The normalizers learn from the input image's characteristics and apply them autonomously to generated objects.
Solution Approach 2:
The patent transforms the extrapolation process from simple texture replication to semantic-aware generation by changing key parameters: using instance segmentation maps, applying instance-aware normalizers, and generating diverse objects with proper semantic understanding rather than just copying existing patterns.
2Reliability
If manual methods are used to add diverse objects and features, then saliency and realism are improved, but labor intensity increases
Solution Approach 1:
The patent replaces manual mechanical editing operations with an automated neural network system. The instance-aware context normalizers and semantic segmentation networks automatically perform tasks that would otherwise require manual intervention, substituting human labor with intelligent algorithms.
Solution Approach 2:
The system introduces instance segmentation maps and instance-aware context normalizers as intermediaries between the input image and the final extrapolated result. These intermediaries carry semantic information that enables automated generation of realistic objects without manual input.
3Speed
If simple texture extension is used for extrapolation, then processing speed is improved, but visual complexity and diversity deteriorate
Solution Approach 1:
The system performs preliminary instance segmentation and context normalization before generating the final extrapolated image. By preparing instance masks and learning context characteristics in advance, the system enables rapid generation of diverse objects without sacrificing visual complexity.
Solution Approach 2:
The patent adds semantic dimensionality to the extrapolation process by incorporating instance segmentation maps and semantic labels. This transforms the process from 2D pixel manipulation to multi-dimensional semantic-aware generation, enabling diverse object creation while maintaining speed.
Data Source
AI summary
Certain aspects and features of this disclosure relate to semantically-aware image extrapolation. In one example, an input image is segmented to produce an input segmentation map of object instances in the input image. An object generation network is used to generate an extrapolated semantic label map for an extrapolated image. The extrapolated semantic label map includes instances in the original image and instances that will appear in an outpainted region of the extrapolated image. A panoptic label map is derived from coordinates of output instances in the extrapolated image and used to identify partial instances and boundaries. Instance-aware context normalization is used to apply one or more characteristics from the input image to the outpainted region to maintain semantic continuity. The extrapolated image includes the original image and the outpainted region and can be rendered or stored for future use.