EffectErase: Joint Video Object Removal and Insertion for High-Quality Effect Erasing
Yang Fu, Yike Zheng, Ziyun Dai, Henghui Ding

TL;DR
EffectErase is a novel method for high-quality video object removal and effect erasing, leveraging a new large-scale dataset VOR and reciprocal learning to handle complex effects and backgrounds.
Contribution
The paper introduces VOR, a comprehensive dataset for object effect removal, and EffectErase, a new effect-aware video object removal approach with reciprocal learning and task guidance.
Findings
EffectErase outperforms existing methods in effect erasing quality.
VOR dataset enables robust training and evaluation across diverse scenarios.
The reciprocal learning scheme improves effect localization and background synthesis.
Abstract
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 (Video Object Removal), 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…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Visual Attention and Saliency Detection · Image Enhancement Techniques
