Panoptic Image Inpainting for Instance-Aware Object Separation
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Solution Overview
Problem
Conventional digital image inpainting systems suffer from inaccuracies, inflexibilities, and inefficiencies, particularly in handling regions of pixels that share a semantic label but depict different object instances, often merging separate objects into one large structure and requiring excessive user interactions.
Innovation Solution
The panoptic inpainting system employs a panoptic inpainting neural network that utilizes panoptic segmentation maps to differentiate between instances of objects with shared semantic labels, incorporating a semantic discriminator with a unique architecture to generate realistic images, and provides a simplified user interface for efficient inpainting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional inpainting models are used to fill missing pixels, then the inpainting process can be automated, but the accuracy deteriorates when handling regions with multiple object instances sharing the same semantic label
Solution Approach 1:
The patent applies segmentation by dividing the inpainting task into instance-level segments using panoptic segmentation maps. Each object instance is identified and separated into distinct regions, allowing the generative model to process each instance independently rather than treating all pixels of the same semantic label as a single region. This resolves the contradiction by maintaining automation while improving accuracy through instance differentiation.
Solution Approach 2:
The patent implements local quality by applying different processing strategies to different instance regions within the same semantic category. Each object instance receives customized inpainting based on its specific panoptic label and contextual information, rather than applying a uniform approach to all regions. This enables automated processing while achieving high accuracy for each local region.
2Manufacturing precision
If panoptic segmentation maps are used to differentiate object instances, then the inpainting accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent applies preliminary action by generating panoptic segmentation maps before the inpainting process. The segmentation step is performed in advance to identify and label all object instances, creating a structured guide for the subsequent inpainting operation. This separates the complex segmentation task from the inpainting task, allowing each to be optimized independently while maintaining overall accuracy.
Solution Approach 2:
The panoptic segmentation map serves as an intermediary between the input image and the inpainting model. Rather than directly processing raw pixels, the system uses the segmentation map as an intermediate representation that encodes instance boundaries and semantic information. This intermediary structure simplifies the inpainting task by providing pre-organized instance-level guidance.
3Speed
If conventional inpainting systems process regions with shared semantic labels, then the processing speed is maintained, but the user interactions increase due to inflexibility
Solution Approach 1:
The patent implements dynamics by making the inpainting system adaptive to instance-level variations through panoptic guidance. The system dynamically adjusts its processing based on the detected object instances and their relationships, rather than following a fixed rigid workflow. This enables the system to handle diverse scenarios automatically, reducing the need for user intervention while maintaining processing efficiency.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media for panoptically guiding digital image inpainting utilizing a panoptic inpainting neural network. In some embodiments, the disclosed systems utilize a panoptic inpainting neural network to generate an inpainted digital image according to panoptic segmentation map that defines pixel regions corresponding to different panoptic labels. In some cases, the disclosed systems train a neural network utilizing a semantic discriminator that facilitates generation of digital images that are realistic while also conforming to a semantic segmentation. The disclosed systems generate and provide a panoptic inpainting interface to facilitate user interaction for inpainting digital images. In certain embodiments, the disclosed systems iteratively update an inpainted digital image based on changes to a panoptic segmentation map.