From Understanding to Erasing: Towards Complete and Stable Video Object Removal

• URL: http://arxiv.org/abs/2604.01693v1
• Date: Thu, 02 Apr 2026 06:50:39 GMT
• Title: From Understanding to Erasing: Towards Complete and Stable Video Object Removal
• Abstract summary: Video object removal aims to eliminate target objects from videos while plausibly completing missing regions and preservingtemporal consistency.<n>It remains challenging to remove object-induced side effects without compromising overall coherence.<n>This paper introduces understanding into erasing from two complementary perspectives.
• Score: 8.035439287403983
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video object removal aims to eliminate target objects from videos while plausibly completing missing regions and preserving spatio-temporal consistency. Although diffusion models have recently advanced this task, it remains challenging to remove object-induced side effects (e.g., shadows, reflections, and illumination changes) without compromising overall coherence. This limitation stems from the insufficient physical and semantic understanding of the target object and its interactions with the scene. In this paper, we propose to introduce understanding into erasing from two complementary perspectives. Externally, we introduce a distillation scheme that transfers the relationships between objects and their induced effects from vision foundation models to video diffusion models. Internally, we propose a framewise context cross-attention mechanism that grounds each denoising block in informative, unmasked context surrounding the target region. External and internal guidance jointly enable our model to understand the target object, its induced effects, and the global background context, resulting in clear and coherent object removal. Extensive experiments demonstrate our state-of-the-art performance, and we establish the first real-world benchmark for video object removal to facilitate future research and community progress. Our code, data, and models are available at: https://github.com/WeChatCV/UnderEraser.

Related papers

• BeyondMasks: Evaluating Causal and Physical Consistency in Video Object Removal [27.338971504674983]
  We introduce BeyondMasks, a paired benchmark for causally consistent video object removal.<n>The dataset spans diverse photometric, geometric, volumetric, and dynamic interactions.<n>We propose CORE, a structured vision language model based evaluation protocol.
  arXiv  Detail & Related papers  (2026-08-20T14:37:40Z)
• EffectLearner: World-Aware Object-Effect Reasoning for Real-World Video Object Removal [43.705438947753656]
  EffectLearner is a framework that learns object-effect correspondences implicitly from effect categories and fixed data generalizations.<n>We further construct EffectWorld, a dataset specifically designed for complex object-induced effects, and introduce a progressive training curriculum that combines common supervision with complex data.
  arXiv  Detail & Related papers  (2026-08-06T03:34:19Z)
• VOID: Video Object and Interaction Deletion [50.155301620235285]
  Existing video object removal methods excel at inpainting content "behind" the object and correcting appearance-level artifacts such as shadows and reflections.<n>We present VOID, a video object removal framework designed to perform physically-plausible inpainting in complex scenarios.
  arXiv  Detail & Related papers  (2026-04-02T17:36:53Z)
• EffectErase: Joint Video Object Removal and Insertion for High-Quality Effect Erasing [50.43992550991499]
  Video object removal aims to eliminate dynamic target objects and their visual effects, such as deformation, shadows, and reflections, while restoring seamless backgrounds.<n>Recent diffusion-based video inpainting and object removal methods can remove the objects but often struggle to erase these effects and to synthesize coherent backgrounds.<n>We introduce VOR (Video Object Removal), a large-scale dataset that provides diverse paired videos.<n>We propose EffectErase, an effect-aware video object removal method that treats video object insertion as the inverse auxiliary task within a reciprocal learning scheme.
  arXiv  Detail & Related papers  (2026-03-19T17:59:22Z)
• GenHOI: Towards Object-Consistent Hand-Object Interaction with Temporally Balanced and Spatially Selective Object Injection [54.879037588415656]
  GenHOI is a lightweight augmentation to pretrained video generation models.<n>It injects reference-object information in a temporally balanced and spatially selective manner.<n>GenHOI significantly outperforms state-of-the-art HOI reenactment and all-in-one video editing methods.
  arXiv  Detail & Related papers  (2026-03-06T09:01:09Z)
• ObjectClear: Complete Object Removal via Object-Effect Attention [56.2893552300215]
  We introduce a new dataset for OBject-Effect Removal, named OBER, which provides paired images with and without object effects, along with precise masks for both objects and their associated visual artifacts.<n>We propose a novel framework, ObjectClear, which incorporates an object-effect attention mechanism to guide the model toward the foreground removal regions by learning attention masks.<n>Experiments demonstrate that ObjectClear outperforms existing methods, achieving improved object-effect removal quality and background fidelity, especially in complex scenarios.
  arXiv  Detail & Related papers  (2025-05-28T17:51:17Z)
• OmniEraser: Remove Objects and Their Effects in Images with Paired Video-Frame Data [21.469971783624402]
  In this paper, we propose Video4Removal, a large-scale dataset comprising over 100,000 high-quality samples with realistic object shadows and reflections.<n>By constructing object-background pairs from video frames with off-the-shelf vision models, the labor costs of data acquisition can be significantly reduced.<n>To avoid generating shape-like artifacts and unintended content, we propose Object-Background Guidance.<n>We present OmniEraser, a novel method that seamlessly removes objects and their visual effects using only object masks as input.
  arXiv  Detail & Related papers  (2025-01-13T15:12:40Z)
• SOS! Self-supervised Learning Over Sets Of Handled Objects In Egocentric Action Recognition [35.4163266882568]
  We introduce Self-Supervised Learning Over Sets (SOS) to pre-train a generic Objects In Contact (OIC) representation model. Our OIC significantly boosts the performance of multiple state-of-the-art video classification models.
  arXiv  Detail & Related papers  (2022-04-10T23:27:19Z)
• Discovering Objects that Can Move [55.743225595012966]
  We study the problem of object discovery -- separating objects from the background without manual labels. Existing approaches utilize appearance cues, such as color, texture, and location, to group pixels into object-like regions. We choose to focus on dynamic objects -- entities that can move independently in the world.
  arXiv  Detail & Related papers  (2022-03-18T21:13:56Z)
• Weakly-Supervised Video Object Grounding via Causal Intervention [82.68192973503119]
  We target at the task of weakly-supervised video object grounding (WSVOG), where only video-sentence annotations are available during model learning. It aims to localize objects described in the sentence to visual regions in the video, which is a fundamental capability needed in pattern analysis and machine learning.
  arXiv  Detail & Related papers  (2021-12-01T13:13:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.