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.
翻译:图像构成的目的是通过将一个图像中的对象插入到另一个背景图像中,从而产生现实的复合图像,在这种情况下,插入对象的放置(例如位置、大小、封闭性)可能不合理,会大大降低复合图像的质量。虽然有些作品试图学习对象放置以创建现实的复合图像,但并不侧重于评估物体放置的可取性。在本文中,我们侧重于目标定位评估任务,以核实复合图像在对象放置方面是否合理。为了完成这项任务,我们建立了第一个目标定位评估数据集,由复合图像及其合理性标签组成。我们还为这项任务提出了一个简单而有效的基线。数据集可在https://github.com/bcmi/Object-Placement-Aview-Dataset-OPA查阅。