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[AAAI 2022] The first dataset on foreground object shadow generation for image composition in real-world scenes. The code used in our paper "Shadow Generation for Composite Image in Real-world Scenes", AAAI2022. Useful for shadow generation, shadow removal, image composition, etc.
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AIGC and Image Composition
2 projects | /r/computervision | 6 Apr 2023

When the condition information is an exemplar image, we know all the attributes of the foreground object. Some attributes are consistent with the background, whereas some other attributes are inconsistent with the background. LC-AIGC would adjust all the attributes to make them compatible with the background, but this could lead to undesired changes. In practice, we usually hope to preserve partial or all attributes which are compatible with the background, and make as few changes as possible. Previously, our lab decomposes image composition task into several subtasks, achieving relatively good controllability. For example, image harmonization only adjusts the foreground illumination, shadow generation only generates shadow for the foreground object, object placement only seeks for reasonable placement (e.g., bounding box, geometric transformation) for the foreground object. Relevant contents are summarized in https://github.com/bcmi/Awesome-Image-Composition.

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