OPA: Object Placement Assessment Dataset
This work addresses the need for evaluating object placement realism in image composition, which is incremental as it introduces a new dataset and baseline for a previously underexplored task.
The paper tackles the problem of assessing the plausibility of object placement in composite images, constructing the first Object Placement Assessment (OPA) dataset with composite images and rationality labels, and proposes a simple baseline method for this task.
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.