# Imagining the Unseen: Generative Location Modeling for Object Placement

arXiv: https://arxiv.org/abs/2410.13564 · HTML: https://mlsanity.com/paper/2410.13564/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 43 of its 53 references that are in the map.
- First posted on arXiv 17 Oct 2024.

## Builds on (most cited first, 30 of 43)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [LVIS: A Dataset for Large Vocabulary Instance Segmentation](https://mlsanity.com/paper/1908.03195/) (2019)
- [GLIGEN: Open-Set Grounded Text-to-Image Generation](https://mlsanity.com/paper/2301.07093/) (2023)
- [MagicBrush: A Manually Annotated Dataset for Instruction-Guided Image Editing](https://mlsanity.com/paper/2306.10012/) (2023)
- [ImageNet-21K Pretraining for the Masses](https://mlsanity.com/paper/2104.10972/) (2021)
- [Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://mlsanity.com/paper/2211.13227/) (2022)
- [LayoutGPT: Compositional Visual Planning and Generation with Large Language Models](https://mlsanity.com/paper/2305.15393/) (2023)
- [LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models](https://mlsanity.com/paper/2305.13655/) (2023)
- [BrushNet: A Plug-and-Play Image Inpainting Model with Decomposed Dual-Branch Diffusion](https://mlsanity.com/paper/2403.06976/) (2024)
- [HQ-Edit: A High-Quality Dataset for Instruction-based Image Editing](https://mlsanity.com/paper/2404.09990/) (2024)
- [HIVE: Harnessing Human Feedback for Instructional Visual Editing](https://mlsanity.com/paper/2303.09618/) (2023)
- [A Task is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting](https://mlsanity.com/paper/2312.03594/) (2023)
- [Grounded Text-to-Image Synthesis with Attention Refocusing](https://mlsanity.com/paper/2306.05427/) (2023)
- [LayoutDM: Discrete Diffusion Model for Controllable Layout Generation](https://mlsanity.com/paper/2303.08137/) (2023)
- [LayoutTransformer: Layout Generation and Completion with Self-attention](https://mlsanity.com/paper/2006.14615/) (2020)
- [LayoutVAE: Stochastic Scene Layout Generation From a Label Set](https://mlsanity.com/paper/1907.10719/) (2019)
- [Modeling Visual Context is Key to Augmenting Object Detection Datasets](https://mlsanity.com/paper/1807.07428/) (2018)
- [ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing](https://mlsanity.com/paper/1803.01837/) (2018)
- [Ranni: Taming Text-to-Image Diffusion for Accurate Instruction Following](https://mlsanity.com/paper/2311.17002/) (2023)

## Built on by (most cited first, 4 of 4)

- [Controllable 3D Placement of Objects with Scene-Aware Diffusion Models](https://mlsanity.com/paper/2506.21446/) (2025)
- [Scene-Aware Location Modeling for Data Augmentation in Automotive Object Detection](https://mlsanity.com/paper/2504.17076/) (2025)
- [The 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real): Methods and Results](https://mlsanity.com/paper/2509.10463/) (2025)
- [HiddenObjects: Scalable Diffusion-Distilled Spatial Priors for Object Placement](https://mlsanity.com/paper/2604.10675/) (2026)

## Abstract (arXiv)

Location modeling, or determining where non-existing objects could feasibly appear in a scene, has the potential to benefit numerous computer vision tasks, from automatic object insertion to scene creation in virtual reality. Yet, this capability remains largely unexplored to date. In this paper, we develop a generative location model that, given an object class and an image, learns to predict plausible bounding boxes for such an object. Our approach first tokenizes the image and target object class, then decodes bounding box coordinates through an autoregressive transformer. This formulation effectively addresses two core challenges in locatio modeling: the inherent one-to-many nature of plausible locations, and the sparsity of existing location modeling datasets, where fewer than 1% of valid placements are labeled. Furthermore, we incorporate Direct Preference Optimization to leverage negative labels, refining the spatial predictions. Empirical evaluations reveal that our generative location model achieves superior placement accuracy on the OPA dataset as compared to discriminative baselines and image composition approaches. We further test our model in the context of object insertion, where it proposes locations for an off-the-shelf inpainting model to render objects. In this respect, our proposal exhibits improved visual coherence relative to state-of-the-art instruction-tuned editing methods, demonstrating a high-performing location model's utility in a downstream application.