# OPA: Object Placement Assessment Dataset

arXiv: https://arxiv.org/abs/2107.01889 · HTML: https://mlsanity.com/paper/2107.01889/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 29 references that are in the map.
- First posted on arXiv 5 Jul 2021.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation](https://mlsanity.com/paper/2012.07177/) (2020)
- [DoveNet: Deep Image Harmonization via Domain Verification](https://mlsanity.com/paper/1911.13239/) (2019)
- [Deep Image Harmonization](https://mlsanity.com/paper/1703.00069/) (2017)
- [Modeling Visual Context is Key to Augmenting Object Detection Datasets](https://mlsanity.com/paper/1807.07428/) (2018)
- [InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting](https://mlsanity.com/paper/1908.07801/) (2019)
- [ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing](https://mlsanity.com/paper/1803.01837/) (2018)
- [Making Images Real Again: A Comprehensive Survey on Deep Image Composition](https://mlsanity.com/paper/2106.14490/) (2021)
- [Learning to Generate Synthetic Data via Compositing](https://mlsanity.com/paper/1904.05475/) (2019)
- [Synthesizing Training Data for Object Detection in Indoor Scenes](https://mlsanity.com/paper/1702.07836/) (2017)
- [Learning to Segment via Cut-and-Paste](https://mlsanity.com/paper/1803.06414/) (2018)
- [Context-Aware Synthesis and Placement of Object Instances](https://mlsanity.com/paper/1812.02350/) (2018)
- [Where and Who? Automatic Semantic-Aware Person Composition](https://mlsanity.com/paper/1706.01021/) (2017)
- [Data Augmentation for Object Detection via Progressive and Selective Instance-Switching](https://mlsanity.com/paper/1906.00358/) (2019)
- [What and Where: A Context-based Recommendation System for Object Insertion](https://mlsanity.com/paper/1811.09783/) (2018)

## Built on by (most cited first, 28 of 28)

- [TF-ICON: Diffusion-Based Training-Free Cross-Domain Image Composition](https://mlsanity.com/paper/2307.12493/) (2023)
- [Paint by Inpaint: Learning to Add Image Objects by Removing Them First](https://mlsanity.com/paper/2404.18212/) (2024)
- [Learning Object Placement via Dual-path Graph Completion](https://mlsanity.com/paper/2207.11464/) (2022)
- [Iterative Multi-granular Image Editing using Diffusion Models](https://mlsanity.com/paper/2309.00613/) (2023)
- [PrimeComposer: Faster Progressively Combined Diffusion for Image Composition with Attention Steering](https://mlsanity.com/paper/2403.05053/) (2024)
- [Thinking Outside the BBox: Unconstrained Generative Object Compositing](https://mlsanity.com/paper/2409.04559/) (2024)
- [SmartMask: Context Aware High-Fidelity Mask Generation for Fine-grained Object Insertion and Layout Control](https://mlsanity.com/paper/2312.05039/) (2023)
- [TopNet: Transformer-based Object Placement Network for Image Compositing](https://mlsanity.com/paper/2304.03372/) (2023)
- [Fast Object Placement Assessment](https://mlsanity.com/paper/2205.14280/) (2022)
- [Text2Place: Affordance-aware Text Guided Human Placement](https://mlsanity.com/paper/2407.15446/) (2024)
- [Scene-aware Human Pose Generation using Transformer](https://mlsanity.com/paper/2308.02177/) (2023)
- [PlaceIt3D: Language-Guided Object Placement in Real 3D Scenes](https://mlsanity.com/paper/2505.05288/) (2025)
- [Foreground Object Search by Distilling Composite Image Feature](https://mlsanity.com/paper/2308.04990/) (2023)
- [SAC-GAN: Structure-Aware Image Composition](https://mlsanity.com/paper/2112.06596/) (2021)
- [Imagining the Unseen: Generative Location Modeling for Object Placement](https://mlsanity.com/paper/2410.13564/) (2024)
- [DiffPop: Plausibility-Guided Object Placement Diffusion for Image Composition](https://mlsanity.com/paper/2406.07852/) (2024)
- [BOOTPLACE: Bootstrapped Object Placement with Detection Transformers](https://mlsanity.com/paper/2503.21991/) (2025)
- [Object Placement for Anything](https://mlsanity.com/paper/2504.12029/) (2025)
- [Deep Image Composition Meets Image Forgery](https://mlsanity.com/paper/2404.02897/) (2024)
- [Disharmony: Forensics using Reverse Lighting Harmonization](https://mlsanity.com/paper/2501.10212/) (2025)
- [CAD2DMD-SET: Synthetic Generation Tool of Digital Measurement Device CAD Model Datasets for fine-tuning Large Vision-Language Models](https://mlsanity.com/paper/2508.21732/) (2025)
- [Beyond Realism: Learning the Art of Expressive Composition with StickerNet](https://mlsanity.com/paper/2511.20957/) (2025)
- [VSDiffusion: Taming Ill-Posed Shadow Generation via Visibility-Constrained Diffusion](https://mlsanity.com/paper/2603.08020/) (2026)
- [CatalogStitch: Dimension-Aware and Occlusion-Preserving Object Compositing for Catalog Image Generation](https://mlsanity.com/paper/2604.08836/) (2026)
- [HiddenObjects: Scalable Diffusion-Distilled Spatial Priors for Object Placement](https://mlsanity.com/paper/2604.10675/) (2026)
- [Learning to Understand Body Language from Flight through Robust 3D Avatar Placing](https://mlsanity.com/paper/2607.27865/) (2026)
- [presto: Efficient, Training-free, and Open-world Object Placement via Imaginary Search](https://mlsanity.com/paper/2608.21543/) (2026)
- [Hybrid Generative-Discriminative Object Placement](https://mlsanity.com/paper/2608.22692/) (2026)

## Abstract (arXiv)

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.