ReMOVE: A Reference-free Metric for Object Erasure
On the map: Diffusion models → 3D, point cloud and scene generation
Abstract (arXiv)
We introduce textttReMOVE, a novel reference-free metric for assessing object erasure efficacy in diffusion-based image editing models post-generation. Unlike existing measures such as LPIPS and CLIPScore, textttReMOVE addresses the challenge of evaluating inpainting without a reference image, common in practical scenarios. It effectively distinguishes between object removal and replacement. This is a key issue in diffusion models due to stochastic nature of image generation. Traditional metrics fail to align with the intuitive definition of inpainting, which aims for (1) seamless object removal within masked regions (2) while preserving the background continuity. textttReMOVE not only correlates with state-of-the-art metrics and aligns with human perception but also captures the nuanced aspects of the inpainting process, providing a finer-grained evaluation of the generated outputs.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models. Builds on 22 of its 40 references that are in the map. First posted on arXiv 1 Sep 2024.
Builds on
22 of 40 references.
Closest ancestors · 3
Same direction, newest first
- 2023Magicremover: Tuning-free Text-guided Image inpainting with Diffusion Models4 Oct
- 2023Inst-Inpaint: Instructing to Remove Objects with Diffusion Models6 Apr
- 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Models24 Jan
Other references · 19
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
- 2014Microsoft COCO: Common Objects in Contextcited by 17,498
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
- 2023Segment Anythingcited by 8,158
- 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,310
- 2021Zero-Shot Text-to-Image Generationcited by 4,276
- 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,595
- 2022InstructPix2Pix: Learning to Follow Image Editing Instructionscited by 2,591
- 2021CLIPScore: A Reference-free Evaluation Metric for Image Captioningcited by 2,388
- 2021Blended Diffusion for Text-driven Editing of Natural Imagescited by 924
- 2021Resolution-robust Large Mask Inpainting with Fourier Convolutionscited by 730
7 more
- 2022DiffEdit: Diffusion-based semantic image editing with mask guidancecited by 643
- 2022Blended Latent Diffusioncited by 430
- 2023AnyDoor: Zero-shot Object-level Image Customizationcited by 411
- 2024Diffusion Model-Based Image Editing: A Surveycited by 212
- 2021From Show to Tell: A Survey on Deep Learning-based Image Captioningcited by 137
- 2023EditVal: Benchmarking Diffusion Based Text-Guided Image Editing Methodscited by 52
- 2024LoMOE: Localized Multi-Object Editing via Multi-Diffusioncited by 11
Built on by
Citing papers per year: 2025: 4; 2026: 8.
12 papers.
- 2025MiniMax-Remover: Taming Bad Noise Helps Video Object Removalcited by 42
- 2025OmniPaint: Mastering Object-Oriented Editing via Disentangled Insertion-Removal Inpaintingcited by 38
- 2025SmartEraser: Remove Anything from Images using Masked-Region Guidancecited by 24
- 2025Precise Object and Effect Removal with Adaptive Target-Aware Attentioncited by 13
- 2026From Ideal to Real: Stable Video Object Removal under Imperfect Conditionscited by 4
- 2026WorldAct: Activating Monolithic 3D Worlds into Interactive-Ready Object-Centric Scenescited by 2
- 2026Geometric Image Editing via Effects-Sensitive In-Context Inpainting with Diffusion Transformerscited by 1
- 2026DORS: Dynamic Attention Routing for Diffusion-based Object Removal in Dense Scenescited by 1
- 2026BeyondMasks: Evaluating Causal and Physical Consistency in Video Object Removalcited by 1
- 2026You Only Erase Once: Erasing Anything without Bringing Unexpected Contentcited by 0
- 2026AdaEraser: Training-Free Object Removal via Adaptive Attention Suppressioncited by 0
- 2026VOR-Bench: A Human Perception-Driven Benchmark for Video Object Removalcited by 0
- 2026VOR-Bench: A Human Perception-Driven Benchmark for Video Object Removal15 Sep 2026
- 2026BeyondMasks: Evaluating Causal and Physical Consistency in Video Object Removal20 Aug 2026
- 2026DORS: Dynamic Attention Routing for Diffusion-based Object Removal in Dense Scenes18 Jul 2026
- 2026AdaEraser: Training-Free Object Removal via Adaptive Attention Suppression15 May 2026
- 2026WorldAct: Activating Monolithic 3D Worlds into Interactive-Ready Object-Centric Scenes15 May 2026
- 2026You Only Erase Once: Erasing Anything without Bringing Unexpected Content29 Mar 2026
- 2026From Ideal to Real: Stable Video Object Removal under Imperfect Conditions10 Mar 2026
- 2026Geometric Image Editing via Effects-Sensitive In-Context Inpainting with Diffusion Transformers9 Feb 2026
- 2025MiniMax-Remover: Taming Bad Noise Helps Video Object Removal30 May 2025
- 2025Precise Object and Effect Removal with Adaptive Target-Aware Attention28 May 2025
- 2025OmniPaint: Mastering Object-Oriented Editing via Disentangled Insertion-Removal Inpainting11 Mar 2025
- 2025SmartEraser: Remove Anything from Images using Masked-Region Guidance14 Jan 2025
Similar, not cited
Close in wording, no citation either way
- 2024Paint by Inpaint: Learning to Add Image Objects by Removing Them First
- 2025CrimEdit: Controllable Editing for Counterfactual Object Removal, Insertion, and Movement
- 2026ClickRemoval: An Interactive Open-Source Tool for Object Removal in Diffusion Models
- 2026From Understanding to Erasing: Towards Complete and Stable Video Object Removal
- 2026VOID: Video Object and Interaction Deletion
- 2026OSOR: One-Step Diffusion Inpainting for Effect-Aware Object Removal
- 2024CLIPAway: Harmonizing Focused Embeddings for Removing Objects via Diffusion Models
- 2026Editing on the Generative Manifold: A Theoretical and Empirical Study of General Diffusion-Based Image Editing Trade-offs