EffectErase：联合视频物体移除与插入实现高质量效应擦除
EffectErase: Joint Video Object Removal and Insertion for High-Quality Effect Erasing
把视频物体插入当作移除的逆辅助任务做互逆学习，并用任务感知区域引导把学习集中在形变、阴影、反射等效应区域，这一思路可直接迁移到视频修复、阴影/反射去除等需要定位并擦除视觉效应的编辑任务。
原文依据 treats video object insertion as the inverse auxiliary task within a reciprocal learning scheme
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论视频物体移除旨在消除动态目标物体及其视觉效应（如形变、阴影和反射），同时恢复无缝背景。近期基于扩散的视频修复与物体移除方法能够移除物体，但往往难以擦除这些效应并合成连贯的背景。除方法本身的局限外，研究进展还受制于缺乏一个能够系统性地捕捉不同环境下常见物体效应的综合数据集，用于训练与评测。为解决这一问题，我们提出了 VOR（Video Object Removal，视频物体移除），一个大规模数据集，提供多样化的成对视频，每一对包含一个存在目标物体及其效应的视频，以及一个物体与效应均不存在的对应视频，并附带相应的物体掩码。VOR 包含来自实拍与合成来源的 6 万对高质量视频，覆盖五种效应类型，涵盖广泛的物体类别以及复杂、动态的多物体场景。基于 VOR，我们提出了 EffectErase，一种效应感知的视频物体移除方法，它将视频物体插入视为互逆学习框架中的逆辅助任务。该模型包含任务感知区域引导，将学习聚焦于受影响的区域并支持灵活的任务切换。随后，一个插入—移除一致性目标鼓励互补行为，并对效应区域与结构线索进行共享定位。在 VOR 上训练后，EffectErase 在大量实验中取得了优越性能，能够在多样场景中实现高质量的视频物体效应擦除。
英文原文摘要 1681 字符
Video object removal aims to eliminate dynamic target objects and their visual effects, such as deformation, shadows, and reflections, while restoring seamless backgrounds. 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. Beyond method limitations, progress is further hampered by the lack of a comprehensive dataset that systematically captures common object effects across varied environments for training and evaluation. To address this, we introduce **VOR** (**V**ideo **O**bject **R**emoval), a large-scale dataset that provides diverse paired videos, each consisting of one video where the target object is present with its effects and a counterpart where the object and effects are absent, with corresponding object masks. VOR contains 60k high-quality video pairs from captured and synthetic sources, covers five effects types, and spans a wide range of object categories as well as complex, dynamic multi-object scenes. Building on VOR, 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. The model includes task-aware region guidance that focuses learning on affected areas and enables flexible task switching. Then, an insertion-removal consistency objective that encourages complementary behaviors and shared localization of effect regions and structural cues. Trained on VOR, EffectErase achieves superior performance in extensive experiments, delivering high-quality video object effect erasing across diverse scenarios.