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OPA: Object Placement Assessment Dataset

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arxiv 2107.01889 v3 pith:5435TGK4 submitted 2021-07-05 cs.CV

OPA: Object Placement Assessment Dataset

classification cs.CV
keywords objectplacementimagecompositeassessmentdatasettaskfocus
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. HiddenObjects: Scalable Diffusion-Distilled Spatial Priors for Object Placement

    cs.CV 2026-04 unverdicted novelty 7.0

    A diffusion-based pipeline creates a 27M-annotation dataset of object placements that outperforms human annotations and baselines on image editing tasks, then distills it into a fast model.

  2. CatalogStitch: Dimension-Aware and Occlusion-Preserving Object Compositing for Catalog Image Generation

    cs.CV 2026-04 unverdicted novelty 6.0

    CatalogStitch provides dimension-aware mask computation and occlusion-aware hybrid restoration to automate corrections in generative object compositing for catalog images.