# GraPLUS: Graph-based Placement Using Semantics for Image Composition

arXiv: https://arxiv.org/abs/2503.15761 · HTML: https://mlsanity.com/paper/2503.15761/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 22 references that are in the map.
- First posted on arXiv 20 Mar 2025.

## Builds on (most cited first, 13 of 13)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Neural Motifs: Scene Graph Parsing with Global Context](https://mlsanity.com/paper/1711.06640/) (2017)
- [Unbiased Scene Graph Generation from Biased Training](https://mlsanity.com/paper/2002.11949/) (2020)
- [InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting](https://mlsanity.com/paper/1908.07801/) (2019)
- [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)
- [Learning Object Placement via Dual-path Graph Completion](https://mlsanity.com/paper/2207.11464/) (2022)
- [Data Augmentation for Object Detection via Progressive and Selective Instance-Switching](https://mlsanity.com/paper/1906.00358/) (2019)
- [TopNet: Transformer-based Object Placement Network for Image Compositing](https://mlsanity.com/paper/2304.03372/) (2023)
- [What and Where: A Context-based Recommendation System for Object Insertion](https://mlsanity.com/paper/1811.09783/) (2018)
- [DiffPop: Plausibility-Guided Object Placement Diffusion for Image Composition](https://mlsanity.com/paper/2406.07852/) (2024)

## Abstract (arXiv)

We present GraPLUS (Graph-based Placement Using Semantics), a novel framework for plausible object placement in images that leverages scene graphs and large language models. Our approach uniquely combines graph-structured scene representation with semantic understanding to determine contextually appropriate object positions. The framework employs GPT-2 to transform categorical node and edge labels into rich semantic embeddings that capture both definitional characteristics and typical spatial contexts, enabling nuanced understanding of object relationships and placement patterns. GraPLUS achieves placement accuracy of 92.1% and an FID score of 28.83 on the OPA dataset, outperforming state-of-the-art methods by 8.1% while maintaining competitive visual quality. In human evaluation studies involving 964 samples assessed by 19 participants, our method was preferred in 52.1% of cases, significantly outperforming previous approaches. The framework's key innovations include: (i) leveraging pre-trained scene graph models that transfer knowledge from other domains, (ii) edge-aware graph neural networks that process scene semantics through structured relationships, (iii) a cross-modal attention mechanism that aligns categorical embeddings with enhanced scene features, and (iv) a multiobjective training strategy incorporating semantic consistency constraints.