Beyond Image Borders: Learning Feature Extrapolation for Unbounded Image Composition
Xiaoyu Liu, Ming Liu, Junyi Li, Shuai Liu, Xiaotao Wang, Lei Lei,, Wangmeng Zuo

TL;DR
This paper introduces UNIC, a joint framework for unbounded camera view recommendation and image composition that ensures real, high-quality images by extrapolating features and predicting camera adjustments.
Contribution
The proposed UNIC framework uniquely combines view recommendation and image composition, extending the field of view through feature extrapolation for unbounded image editing.
Findings
Effective in unbounded camera view recommendation
Improves image composition quality
Converges to optimal view and composition
Abstract
For improving image composition and aesthetic quality, most existing methods modulate the captured images by striking out redundant content near the image borders. However, such image cropping methods are limited in the range of image views. Some methods have been suggested to extrapolate the images and predict cropping boxes from the extrapolated image. Nonetheless, the synthesized extrapolated regions may be included in the cropped image, making the image composition result not real and potentially with degraded image quality. In this paper, we circumvent this issue by presenting a joint framework for both unbounded recommendation of camera view and image composition (i.e., UNIC). In this way, the cropped image is a sub-image of the image acquired by the predicted camera view, and thus can be guaranteed to be real and consistent in image quality. Specifically, our framework takes the…
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Taxonomy
TopicsVisual Attention and Saliency Detection · Image and Video Quality Assessment · Advanced Image Processing Techniques
