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Found 4 papers, 4 papers with code
Shadow Generation for Composite Image Using Diffusion model
In the realm of image composition, generating realistic shadow for the inserted foreground remains a formidable challenge.
DESOBAv2: Towards Large-scale Real-world Dataset for Shadow Generation
In this work, we focus on generating plausible shadow for the inserted foreground object to make the composite image more realistic.
Fast Object Placement Assessment
However, given a pair of scaled foreground and background, to enumerate all the reasonable locations, existing OPA model needs to place the foreground at each location on the background and pass the obtained composite image through the model one at a time, which is very time-consuming.
OPA: Object Placement Assessment Dataset
Image composition aims to generate realistic composite image by inserting an object from one image into another background image, where the placement (e. g., location, size, occlusion) of inserted object may be unreasonable, which would significantly degrade the quality of the composite image.
