EffectErase: Joint Video Object Removal and Insertion for High-Quality Effect Erasing
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 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.
69bcf8d1081b9ad49af573b6
Yang Fu
claimed_verified
2026-03-23T10:51:36.877Z
65ee73318c82beffd93a228f
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/_VhkbN46gKt7g8AU-YsYX.jpeg
Yang Fu 
aleeyanger
user
aleeyanger
69bcf8d1081b9ad49af573b7
Yike Zheng
69bcf8d1081b9ad49af573b8
Ziyun Dai
69bcf8d1081b9ad49af573b9
Henghui Ding
claimed_verified
2026-03-23T10:51:33.729Z
67ff29ecbf6889a333c69c7a
https://cdn-avatars.huggingface.co/v1/production/uploads/67ff29ecbf6889a333c69c7a/zilMQrxIgUKYvHBVCHaKL.jpeg
Henghui Ding
HenghuiDing
user
HenghuiDing
EffectErase: Joint Video Object Removal and Insertion for High-Quality Effect Erasing
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 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.
Video object removal method EffectErase uses a large-scale dataset VOR to effectively eliminate dynamic objects and their visual effects through reciprocal learning and task-aware guidance.
video inpainting object removal diffusion-based methods reciprocal learning task-aware region guidance insertion-removal consistency objective