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. Although some works attempted to learn object placement to create realistic composite images, they did not focus on assessing the plausibility of object placement. In this paper, we focus on object placement assessment task, which verifies whether a composite image is plausible in terms of the object placement. To accomplish this task, we construct the first Object Placement Assessment (OPA) dataset consisting of composite images and their rationality labels. We also propose a simple yet effective baseline for this task. Dataset is available at https://github.com/bcmi/Object-Placement-Assessment-Dataset-OPA.
@article{arxiv.2107.01889,
title = {OPA: Object Placement Assessment Dataset},
author = {Liu Liu and Zhenchen Liu and Bo Zhang and Jiangtong Li and Li Niu and Qingyang Liu and Liqing Zhang},
journal= {arXiv preprint arXiv:2107.01889},
year = {2022}
}