# Where and Who? Automatic Semantic-Aware Person Composition

arXiv: https://arxiv.org/abs/1706.01021 · HTML: https://mlsanity.com/paper/1706.01021/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2017.

## Built on by (most cited first, 21 of 21)

- [SAGA: Stochastic Whole-Body Grasping with Contact](https://mlsanity.com/paper/2112.10103/) (2021)
- [Making Images Real Again: A Comprehensive Survey on Deep Image Composition](https://mlsanity.com/paper/2106.14490/) (2021)
- [OPA: Object Placement Assessment Dataset](https://mlsanity.com/paper/2107.01889/) (2021)
- [Learning Object Placement via Dual-path Graph Completion](https://mlsanity.com/paper/2207.11464/) (2022)
- [Thinking Outside the BBox: Unconstrained Generative Object Compositing](https://mlsanity.com/paper/2409.04559/) (2024)
- [TopNet: Transformer-based Object Placement Network for Image Compositing](https://mlsanity.com/paper/2304.03372/) (2023)
- [LayoutDETR: Detection Transformer Is a Good Multimodal Layout Designer](https://mlsanity.com/paper/2212.09877/) (2022)
- [Fast Object Placement Assessment](https://mlsanity.com/paper/2205.14280/) (2022)
- [Deep Image Harmonization by Bridging the Reality Gap](https://mlsanity.com/paper/2103.17104/) (2021)
- [Narrator: Towards Natural Control of Human-Scene Interaction Generation via Relationship Reasoning](https://mlsanity.com/paper/2303.09410/) (2023)
- [Scene-aware Human Pose Generation using Transformer](https://mlsanity.com/paper/2308.02177/) (2023)
- [Placing Human Animations into 3D Scenes by Learning Interaction- and Geometry-Driven Keyframes](https://mlsanity.com/paper/2209.06314/) (2022)
- [An Overview of Recent Work in Media Forensics: Methods and Threats](https://mlsanity.com/paper/2204.12067/) (2022)
- [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)
- [Spatial Transformation for Image Composition via Correspondence Learning](https://mlsanity.com/paper/2207.02398/) (2022)
- [MP-Mat: A 3D-and-Instance-Aware Human Matting and Editing Framework with Multiplane Representation](https://mlsanity.com/paper/2504.14606/) (2025)
- [PACE: Data-Driven Virtual Agent Interaction in Dense and Cluttered Environments](https://mlsanity.com/paper/2303.14255/) (2023)
- [DepGAN: Leveraging Depth Maps for Handling Occlusions and Transparency in Image Composition](https://mlsanity.com/paper/2407.11890/) (2024)
- [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 compositing is a method used to generate realistic yet fake imagery by inserting contents from one image to another. Previous work in compositing has focused on improving appearance compatibility of a user selected foreground segment and a background image (i.e. color and illumination consistency). In this work, we instead develop a fully automated compositing model that additionally learns to select and transform compatible foreground segments from a large collection given only an input image background. To simplify the task, we restrict our problem by focusing on human instance composition, because human segments exhibit strong correlations with their background and because of the availability of large annotated data. We develop a novel branching Convolutional Neural Network (CNN) that jointly predicts candidate person locations given a background image. We then use pre-trained deep feature representations to retrieve person instances from a large segment database. Experimental results show that our model can generate composite images that look visually convincing. We also develop a user interface to demonstrate the potential application of our method.