# Learning Object Placement via Dual-path Graph Completion

arXiv: https://arxiv.org/abs/2207.11464 · HTML: https://mlsanity.com/paper/2207.11464/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 44 references that are in the map.
- First posted on arXiv 23 Jul 2022.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [Self-Attention with Relative Position Representations](https://mlsanity.com/paper/1803.02155/) (2018)
- [Convolutional Sequence to Sequence Learning](https://mlsanity.com/paper/1705.03122/) (2017)
- [Image Generation from Scene Graphs](https://mlsanity.com/paper/1804.01622/) (2018)
- [DoveNet: Deep Image Harmonization via Domain Verification](https://mlsanity.com/paper/1911.13239/) (2019)
- [Deep Image Harmonization](https://mlsanity.com/paper/1703.00069/) (2017)
- [ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing](https://mlsanity.com/paper/1803.01837/) (2018)
- [Putting Humans in a Scene: Learning Affordance in 3D Indoor Environments](https://mlsanity.com/paper/1903.05690/) (2019)
- [GP-GAN: Towards Realistic High-Resolution Image Blending](https://mlsanity.com/paper/1703.07195/) (2017)
- [Making Images Real Again: A Comprehensive Survey on Deep Image Composition](https://mlsanity.com/paper/2106.14490/) (2021)
- [Deep Image Blending](https://mlsanity.com/paper/1910.11495/) (2019)
- [Learning to Encode Position for Transformer with Continuous Dynamical Model](https://mlsanity.com/paper/2003.09229/) (2020)
- [High-Resolution Image Harmonization via Collaborative Dual Transformations](https://mlsanity.com/paper/2109.06671/) (2021)
- [Learning a Discriminative Model for the Perception of Realism in Composite Images](https://mlsanity.com/paper/1510.00477/) (2015)
- [Learning to Generate Synthetic Data via Compositing](https://mlsanity.com/paper/1904.05475/) (2019)
- [BargainNet: Background-Guided Domain Translation for Image Harmonization](https://mlsanity.com/paper/2009.09169/) (2020)
- [Synthesizing Training Data for Object Detection in Indoor Scenes](https://mlsanity.com/paper/1702.07836/) (2017)
- [Context-Aware Synthesis and Placement of Object Instances](https://mlsanity.com/paper/1812.02350/) (2018)
- [Shadow Generation for Composite Image in Real-world Scenes](https://mlsanity.com/paper/2104.10338/) (2021)
- [OPA: Object Placement Assessment Dataset](https://mlsanity.com/paper/2107.01889/) (2021)
- [Where and Who? Automatic Semantic-Aware Person Composition](https://mlsanity.com/paper/1706.01021/) (2017)
- [What and Where: A Context-based Recommendation System for Object Insertion](https://mlsanity.com/paper/1811.09783/) (2018)

## Built on by (most cited first, 20 of 20)

- [Iterative Multi-granular Image Editing using Diffusion Models](https://mlsanity.com/paper/2309.00613/) (2023)
- [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)
- [DreamCom: Finetuning Text-guided Inpainting Model for Image Composition](https://mlsanity.com/paper/2309.15508/) (2023)
- [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)
- [Foreground Object Search by Distilling Composite Image Feature](https://mlsanity.com/paper/2308.04990/) (2023)
- [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)
- [AnimateScene: Camera-controllable Animation in Any Scene](https://mlsanity.com/paper/2508.05982/) (2025)
- [Rethinking Referring Object Removal](https://mlsanity.com/paper/2403.09128/) (2024)
- [Exploring Mutual Cross-Modal Attention for Context-Aware Human Affordance Generation](https://mlsanity.com/paper/2502.13637/) (2025)
- [GraPLUS: Graph-based Placement Using Semantics for Image Composition](https://mlsanity.com/paper/2503.15761/) (2025)
- [Beyond Realism: Learning the Art of Expressive Composition with StickerNet](https://mlsanity.com/paper/2511.20957/) (2025)
- [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)

Object placement aims to place a foreground object over a background image with a suitable location and size. In this work, we treat object placement as a graph completion problem and propose a novel graph completion module (GCM). The background scene is represented by a graph with multiple nodes at different spatial locations with various receptive fields. The foreground object is encoded as a special node that should be inserted at a reasonable place in this graph. We also design a dual-path framework upon the structure of GCM to fully exploit annotated composite images. With extensive experiments on OPA dataset, our method proves to significantly outperform existing methods in generating plausible object placement without loss of diversity.