# Thinking Outside the BBox: Unconstrained Generative Object Compositing

arXiv: https://arxiv.org/abs/2409.04559 · HTML: https://mlsanity.com/paper/2409.04559/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 63 of its 73 references that are in the map.
- First posted on arXiv 6 Sep 2024.

## Builds on (most cited first, 30 of 63)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [Imagic: Text-Based Real Image Editing with Diffusion Models](https://mlsanity.com/paper/2210.09276/) (2022)
- [Blended Diffusion for Text-driven Editing of Natural Images](https://mlsanity.com/paper/2111.14818/) (2021)
- [Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation](https://mlsanity.com/paper/2008.00951/) (2020)
- [DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation](https://mlsanity.com/paper/2110.02711/) (2021)
- [Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://mlsanity.com/paper/2211.13227/) (2022)
- [BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editing](https://mlsanity.com/paper/2305.14720/) (2023)
- [Blended Latent Diffusion](https://mlsanity.com/paper/2206.02779/) (2022)
- [AnyDoor: Zero-shot Object-level Image Customization](https://mlsanity.com/paper/2307.09481/) (2023)
- [InstantBooth: Personalized Text-to-Image Generation without Test-Time Finetuning](https://mlsanity.com/paper/2304.03411/) (2023)
- [DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data](https://mlsanity.com/paper/2306.09344/) (2023)
- [SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model](https://mlsanity.com/paper/2212.05034/) (2022)
- [Inpaint Anything: Segment Anything Meets Image Inpainting](https://mlsanity.com/paper/2304.06790/) (2023)
- [YouTube-VOS: Sequence-to-Sequence Video Object Segmentation](https://mlsanity.com/paper/1809.00461/) (2018)
- [MVImgNet: A Large-scale Dataset of Multi-view Images](https://mlsanity.com/paper/2303.06042/) (2023)
- [More Control for Free! Image Synthesis with Semantic Diffusion Guidance](https://mlsanity.com/paper/2112.05744/) (2021)
- [Subject-driven Text-to-Image Generation via Apprenticeship Learning](https://mlsanity.com/paper/2304.00186/) (2023)
- [HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing](https://mlsanity.com/paper/2111.15666/) (2021)
- [TF-ICON: Diffusion-Based Training-Free Cross-Domain Image Composition](https://mlsanity.com/paper/2307.12493/) (2023)
- [Semantic Photo Manipulation with a Generative Image Prior](https://mlsanity.com/paper/2005.07727/) (2020)
- [Pretraining is All You Need for Image-to-Image Translation](https://mlsanity.com/paper/2205.12952/) (2022)
- [EditGAN: High-Precision Semantic Image Editing](https://mlsanity.com/paper/2111.03186/) (2021)

## Built on by (most cited first, 19 of 19)

- [Does FLUX Already Know How to Perform Physically Plausible Image Composition?](https://mlsanity.com/paper/2509.21278/) (2025)
- [ObjectMover: Generative Object Movement with Video Prior](https://mlsanity.com/paper/2503.08037/) (2025)
- [Generative AI for Cel-Animation: A Survey](https://mlsanity.com/paper/2501.06250/) (2025)
- [ObjectMate: A Recurrence Prior for Object Insertion and Subject-Driven Generation](https://mlsanity.com/paper/2412.08645/) (2024)
- [DreamFuse: Adaptive Image Fusion with Diffusion Transformer](https://mlsanity.com/paper/2504.08291/) (2025)
- [Multitwine: Multi-Object Compositing with Text and Layout Control](https://mlsanity.com/paper/2502.05165/) (2025)
- [MV-CoLight: Efficient Object Compositing with Consistent Lighting and Shadow Generation](https://mlsanity.com/paper/2505.21483/) (2025)
- [AIComposer: Any Style and Content Image Composition via Feature Integration](https://mlsanity.com/paper/2507.20721/) (2025)
- [BOOTPLACE: Bootstrapped Object Placement with Detection Transformers](https://mlsanity.com/paper/2503.21991/) (2025)
- [Coeff-Tuning: A Graph Filter Subspace View for Tuning Attention-Based Large Models](https://mlsanity.com/paper/2503.18337/) (2025)
- [PLACID: Identity-Preserving Multi-Object Compositing via Video Diffusion with Synthetic Trajectories](https://mlsanity.com/paper/2602.00267/) (2026)
- [FastInstShadow: A Simple Query-Based Model for Instance Shadow Detection](https://mlsanity.com/paper/2503.07517/) (2025)
- [PICS: Pairwise Image Compositing with Spatial Interactions](https://mlsanity.com/paper/2603.06873/) (2026)
- [LightMover: Generative Light Movement with Color and Intensity Controls](https://mlsanity.com/paper/2603.27209/) (2026)
- [Reflection Generation for Composite Image Using Diffusion Model](https://mlsanity.com/paper/2604.02168/) (2026)
- [HiddenObjects: Scalable Diffusion-Distilled Spatial Priors for Object Placement](https://mlsanity.com/paper/2604.10675/) (2026)
- [BFS: Back-to-Front Layered Image Synthesis via Knowledge Transfer](https://mlsanity.com/paper/2605.24894/) (2026)
- [Chameleon: Style-Content Disentangled Framework for Cross-Domain Object Compositing](https://mlsanity.com/paper/2606.01079/) (2026)
- [RoPEMover: Depth-Aware Object Relocation via Positional Embeddings](https://mlsanity.com/paper/2606.27332/) (2026)

## Abstract (arXiv)

Compositing an object into an image involves multiple non-trivial sub-tasks such as object placement and scaling, color/lighting harmonization, viewpoint/geometry adjustment, and shadow/reflection generation. Recent generative image compositing methods leverage diffusion models to handle multiple sub-tasks at once. However, existing models face limitations due to their reliance on masking the original object during training, which constrains their generation to the input mask. Furthermore, obtaining an accurate input mask specifying the location and scale of the object in a new image can be highly challenging. To overcome such limitations, we define a novel problem of unconstrained generative object compositing, i.e., the generation is not bounded by the mask, and train a diffusion-based model on a synthesized paired dataset. Our first-of-its-kind model is able to generate object effects such as shadows and reflections that go beyond the mask, enhancing image realism. Additionally, if an empty mask is provided, our model automatically places the object in diverse natural locations and scales, accelerating the compositing workflow. Our model outperforms existing object placement and compositing models in various quality metrics and user studies.