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OmniEraser: Remove Objects and Their Effects in Images with Paired Video-Frame Data

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arxiv 2501.07397 v3 pith:ZQ3BQ32Y submitted 2025-01-13 cs.CV

Runpu Wei , Zijin Yin , Shuo Zhang , Lanxiang Zhou , Xueyi Wang , Chao Ban , Tianwei Cao , Hao Sun
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Zhongjiang He Kongming Liang Zhanyu Ma
This is my paper
classification cs.CV
keywords imagesobjecteffectsobjectsomnieraserartifactscontentdata
verification ladder T0 review T1 audit T2 compute T3 formal
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Inpainting algorithms have achieved remarkable progress in removing objects from images, yet still face two challenges: 1) struggle to handle the object's visual effects such as shadow and reflection; 2) easily generate shape-like artifacts and unintended content. In this paper, we propose Video4Removal, a large-scale dataset comprising over 100,000 high-quality samples with realistic object shadows and reflections. By constructing object-background pairs from video frames with off-the-shelf vision models, the labor costs of data acquisition can be significantly reduced. To avoid generating shape-like artifacts and unintended content, we propose Object-Background Guidance, an elaborated paradigm that takes both the foreground object and background images. It can guide the diffusion process to harness richer contextual information. Based on the above two designs, we present OmniEraser, a novel method that seamlessly removes objects and their visual effects using only object masks as input. Extensive experiments show that OmniEraser significantly outperforms previous methods, particularly in complex in-the-wild scenes. And it also exhibits a strong generalization ability in anime-style images. Datasets, models, and codes will be published.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. PROVE: A Perceptual RemOVal cohErence Benchmark for Visual Media

    cs.CV 2026-05 unverdicted novelty 7.0 of 10

    Removal Coherence (RC) metrics, which compare local feature distributions in masked versus background regions via sliding-window MMD, align with human judgments of object-removal quality substantially better than exis...

  2. DORS: Dynamic Attention Routing for Diffusion-based Object Removal in Dense Scenes

    cs.CV 2026-07 conditional novelty 6.0 of 10

    DORS edits self-attention during diffusion denoising so masked pixels ignore similar surrounding instances, reporting large artifact reductions in dense-scene object removal.

  3. EraseLoRA: MLLM-Driven Foreground Exclusion and Background Subtype Aggregation for Dataset-Free Object Removal

    cs.CV 2025-12 conditional novelty 6.0 of 10

    EraseLoRA removes masked objects by having an MLLM separate target, non-target foreground, and background, then test-time LoRA optimization aggregates background subtypes to reconstruct the occluded region.

  4. Mask Consistency Regularization in Object Removal

    cs.CV 2025-09 conditional novelty 6.0 of 10

    A mask-consistency training loss, enforcing equal predictions across dilated and reshaped masks, is proposed to reduce hallucination and mask-shape bias in diffusion-based object removal.

  5. ROSE: Remove Objects with Side Effects in Videos

    cs.CV 2025-08 conditional novelty 6.0 of 10

    A video inpainting model trained on 3D-rendered pairs removes objects together with their shadows, reflections, and other side effects, plus a new benchmark.

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