mlsanityData to 7 Oct 2026

ReMOVE: A Reference-free Metric for Object Erasure

arXiv 2409.00707 ↗First posted 1 Sep 2024cs.CV cs.AI cs.LGcited by 12

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

  1. 2023Magicremover: Tuning-free Text-guided Image inpainting with Diffusion Models4 Oct
  2. 2023Inst-Inpaint: Instructing to Remove Objects with Diffusion Models6 Apr
  3. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Models24 Jan

Other references · 19

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  4. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  5. 2023Segment Anythingcited by 8,158
  6. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,310
  7. 2021Zero-Shot Text-to-Image Generationcited by 4,276
  8. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,595
  9. 2022InstructPix2Pix: Learning to Follow Image Editing Instructionscited by 2,591
  10. 2021CLIPScore: A Reference-free Evaluation Metric for Image Captioningcited by 2,388
  11. 2021Blended Diffusion for Text-driven Editing of Natural Imagescited by 924
  12. 2021Resolution-robust Large Mask Inpainting with Fourier Convolutionscited by 730
7 more
  1. 2022DiffEdit: Diffusion-based semantic image editing with mask guidancecited by 643
  2. 2022Blended Latent Diffusioncited by 430
  3. 2023AnyDoor: Zero-shot Object-level Image Customizationcited by 411
  4. 2024Diffusion Model-Based Image Editing: A Surveycited by 212
  5. 2021From Show to Tell: A Survey on Deep Learning-based Image Captioningcited by 137
  6. 2023EditVal: Benchmarking Diffusion Based Text-Guided Image Editing Methodscited by 52
  7. 2024LoMOE: Localized Multi-Object Editing via Multi-Diffusioncited by 11

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 8.

12 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2024Paint by Inpaint: Learning to Add Image Objects by Removing Them First
  2. 2025CrimEdit: Controllable Editing for Counterfactual Object Removal, Insertion, and Movement
  3. 2026ClickRemoval: An Interactive Open-Source Tool for Object Removal in Diffusion Models
  4. 2026From Understanding to Erasing: Towards Complete and Stable Video Object Removal
  5. 2026VOID: Video Object and Interaction Deletion
  6. 2026OSOR: One-Step Diffusion Inpainting for Effect-Aware Object Removal
  7. 2024CLIPAway: Harmonizing Focused Embeddings for Removing Objects via Diffusion Models
  8. 2026Editing on the Generative Manifold: A Theoretical and Empirical Study of General Diffusion-Based Image Editing Trade-offs