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Awesome-Object-Placement
A curated list of papers, code, and resources pertaining to object placement.
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Awesome-Image-Composition
A curated list of papers, code and resources pertaining to image composition/compositing or object insertion, which aims to generate realistic composite image.
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InfluxDB
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LC-AIGC has one remaining problem, that is, the automatic generation of bounding boxes. In some application scenarios like data augmentation, we need to automatically generate a large number of reasonable bounding boxes. This task is known as object placement, and the relevant materials are summarized in https://github.com/bcmi/Awesome-Object-Placement. Previously, I have also introduced our OPA dataset and GracoNet method.
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.