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Updated: Nov 19, 2025
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Published on: June 27, 2025
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Sketch-Guided Scenery Image Outpainting
Summary
This study introduces sketch-guided image outpainting, enabling custom results by using user sketches. The novel method ensures generated content is realistic and aligns with guiding sketches.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Image Generation
Background:
- Existing image outpainting methods produce random results, failing to meet user needs.
- Controlling the outpainting process for personalized and contextually relevant outputs remains a challenge.
Purpose of the Study:
- To develop a novel sketch-guided image outpainting approach.
- To enable users to generate custom outpainting results guided by their own sketches.
- To improve the realism and sketch consistency of generated image content.
Main Methods:
- An encoder-decoder network architecture is proposed for sketch-guided outpainting.
- Two alignment modules are employed: a holistic alignment for global realism and a sketch alignment for detail consistency.
- The sketch alignment module enforces consistency between generated and ground-truth sketches.
Main Results:
- The proposed method generates realistic and sketch-consistent outpainting results.
- Qualitative and quantitative experiments validate the effectiveness against state-of-the-art generative models.
- The approach demonstrates improved attention to fine details and sensitivity to guiding sketches.
Conclusions:
- Sketch-guided outpainting offers a powerful new direction for conditional image generation.
- The developed method successfully addresses the limitations of existing random outpainting techniques.
- This work pioneers conditional scenery image outpainting with user-provided sketch guidance.
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