Transformer Crop Selection for Compatible Image Synthesis
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Solution Overview
Problem
Conventional image synthesis systems fail to generate composite images with mutually compatible objects, resulting in less realistic and inconsistent outputs that do not meet user expectations.
Innovation Solution
The system employs a sequential crop selection technique using a transformer-based crop selection network trained with contrastive learning, which selects compatible image crops based on content and location information, and an image generator network that implements hierarchical gated convolutions and spatially adaptive normalization to ensure compatibility and realism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional image synthesis systems are used, then image generation can be performed, but the generated composite images lack mutual compatibility between objects and appear unrealistic
Solution Approach 1:
The system performs preliminary crop selection from candidate images before final image synthesis. The crop selection network pre-processes and selects compatible object crops based on scene graph descriptions, ensuring mutual compatibility is established before the synthesis stage. This preliminary action resolves the contradiction by preparing compatible components in advance, leading to both reliable object compatibility and realistic final images.
Solution Approach 2:
The crop selection network acts as an intermediary between the scene graph description and the final image synthesis. It selects compatible object crops that serve as intermediate representations, bridging the gap between textual descriptions and realistic image generation. This intermediary process ensures object compatibility while maintaining realism in the synthesized composite images.
2Adaptability or versatility
If conventional image editing models are used, then image synthesis can be performed, but the synthesized images are not consistent with user requests
Solution Approach 1:
The system uses feedback from the scene graph description to guide the crop selection process. The crop selection network receives feedback about desired objects and their relationships, adjusts its selection accordingly, and produces composite images that are consistent with user requests. This feedback mechanism resolves the contradiction by continuously aligning the synthesis process with user requirements while maintaining high image quality.
Solution Approach 2:
The system changes parameters during the crop selection process, including compatibility scores, object attributes, and spatial relationships. By dynamically adjusting these parameters based on scene graph information, the system achieves both adaptability to user requests and reliability in generating high-quality composite images that accurately reflect the desired scene.
Data Source
AI summary
Systems and methods for image processing are described. Embodiments of the present disclosure identify a first image depicting a first object; identify a plurality of candidate images depicting a second object; select a second image from the plurality of candidate images depicting the second object based on the second image and a sequence of previous images including the first image using a crop selection network trained to select a next compatible image based on the sequence of previous images; and generate a composite image depicting the first object and the second object based on the first image and the second image.