# RORem: Training a Robust Object Remover with Human-in-the-Loop

arXiv: https://arxiv.org/abs/2501.00740 · HTML: https://mlsanity.com/paper/2501.00740/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 61 of its 77 references that are in the map.
- First posted on arXiv 1 Jan 2025.

## Builds on (most cited first, 30 of 61)

- [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)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [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)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://mlsanity.com/paper/2211.09794/) (2022)
- [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://mlsanity.com/paper/2310.04378/) (2023)
- [Resolution-robust Large Mask Inpainting with Fourier Convolutions](https://mlsanity.com/paper/2109.07161/) (2021)
- [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018)
- [The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale](https://mlsanity.com/paper/1811.00982/) (2018)
- [Image Inpainting for Irregular Holes Using Partial Convolutions](https://mlsanity.com/paper/1804.07723/) (2018)
- [Emu Edit: Precise Image Editing via Recognition and Generation Tasks](https://mlsanity.com/paper/2311.10089/) (2023)
- [SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model](https://mlsanity.com/paper/2212.05034/) (2022)
- [Imagen Editor and EditBench: Advancing and Evaluating Text-Guided Image Inpainting](https://mlsanity.com/paper/2212.06909/) (2022)
- [MAT: Mask-Aware Transformer for Large Hole Image Inpainting](https://mlsanity.com/paper/2203.15270/) (2022)

## Built on by (most cited first, 23 of 23)

- [OmniPaint: Mastering Object-Oriented Editing via Disentangled Insertion-Removal Inpainting](https://mlsanity.com/paper/2503.08677/) (2025)
- [OmniEraser: Remove Objects and Their Effects in Images with Paired Video-Frame Data](https://mlsanity.com/paper/2501.07397/) (2025)
- [Diversity-Preserved Distribution Matching Distillation for Fast Visual Synthesis](https://mlsanity.com/paper/2602.03139/) (2026)
- [EffectErase: Joint Video Object Removal and Insertion for High-Quality Effect Erasing](https://mlsanity.com/paper/2603.19224/) (2026)
- [Precise Object and Effect Removal with Adaptive Target-Aware Attention](https://mlsanity.com/paper/2505.22636/) (2025)
- [Follow-Your-Instruction: A Comprehensive MLLM Agent for World Data Synthesis](https://mlsanity.com/paper/2508.05580/) (2025)
- [Long-Horizon Streaming Video Generation via Hybrid Attention with Decoupled Distillation](https://mlsanity.com/paper/2604.10103/) (2026)
- [MatAnyone 2: Scaling Video Matting via a Learned Quality Evaluator](https://mlsanity.com/paper/2512.11782/) (2025)
- [Over++: Generative Video Compositing for Layer Interaction Effects](https://mlsanity.com/paper/2512.19661/) (2025)
- [SynergyAmodal: Deocclude Anything with Text Control](https://mlsanity.com/paper/2504.19506/) (2025)
- [CrimEdit: Controllable Editing for Counterfactual Object Removal, Insertion, and Movement](https://mlsanity.com/paper/2509.23708/) (2025)
- [Refaccade: Editing Object with Given Reference Texture](https://mlsanity.com/paper/2512.04534/) (2025)
- [NeXT-IMDL: Build Benchmark for NeXT-Generation Image Manipulation Detection & Localization](https://mlsanity.com/paper/2512.23374/) (2025)
- [FlashClear: Ultra-Fast Image Content Removal via Efficient Step Distillation and Feature Caching](https://mlsanity.com/paper/2605.09003/) (2026)
- [DORS: Dynamic Attention Routing for Diffusion-based Object Removal in Dense Scenes](https://mlsanity.com/paper/2607.16656/) (2026)
- [MILD: Multi-Layer Diffusion Strategy for Complex and Precise Multi-IP Aware Human Erasing](https://mlsanity.com/paper/2508.06543/) (2025)
- [Search2Motion: Training-Free Object-Level Motion Control via Attention-Consensus Search](https://mlsanity.com/paper/2603.16711/) (2026)
- [SceneForge: Structured World Supervision from 3D Interventions](https://mlsanity.com/paper/2605.14399/) (2026)
- [AdaEraser: Training-Free Object Removal via Adaptive Attention Suppression](https://mlsanity.com/paper/2605.15921/) (2026)
- [Follow-Your-Preference++: Rethinking Preference Alignment for Image Inpainting](https://mlsanity.com/paper/2606.03216/) (2026)
- [Moebius: 0.2B Lightweight Image Inpainting Framework with 10B-Level Performance](https://mlsanity.com/paper/2606.19195/) (2026)
- [ScaleVid: Geometry-Aware Video Object Scaling with Mesh-Free Inference](https://mlsanity.com/paper/2608.12232/) (2026)
- [GIFTBench: Diagnosing Generalization in Image Forgery Localization and Informing Model Design](https://mlsanity.com/paper/2610.01778/) (2026)

## Abstract (arXiv)

Despite the significant advancements, existing object removal methods struggle with incomplete removal, incorrect content synthesis and blurry synthesized regions, resulting in low success rates. Such issues are mainly caused by the lack of high-quality paired training data, as well as the self-supervised training paradigm adopted in these methods, which forces the model to in-paint the masked regions, leading to ambiguity between synthesizing the masked objects and restoring the background. To address these issues, we propose a semi-supervised learning strategy with human-in-the-loop to create high-quality paired training data, aiming to train a Robust Object Remover (RORem). We first collect 60K training pairs from open-source datasets to train an initial object removal model for generating removal samples, and then utilize human feedback to select a set of high-quality object removal pairs, with which we train a discriminator to automate the following training data generation process. By iterating this process for several rounds, we finally obtain a substantial object removal dataset with over 200K pairs. Fine-tuning the pre-trained stable diffusion model with this dataset, we obtain our RORem, which demonstrates state-of-the-art object removal performance in terms of both reliability and image quality. Particularly, RORem improves the object removal success rate over previous methods by more than 18%. The dataset, source code and trained model are available at https://github.com/leeruibin/RORem.