Newest papers

UniVOE: A Removal-Driven Framework for Interaction-Aware Universal Video Object Editing

Shuzhou Yang, Lanqing Guo, Lingen Li, Weiqi Li, Zhao Zhong, Liefeng Bo, Jian Zhang · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31641 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

ビデオオオブジェクト除去および注入は、オブジェクト現在およびオブジェクト不在のビデオ状態間の相互編集操作であるが、トレーニングの難しさに大きく異なります。 除去は、ビデオインパイティングから効果的にブートストラップされ、さらにオブジェクト関連の相互作用の効果を消去するために調整されます。 注入は、オブジェクト外観、ダイナミクスおよびシーン依存の効果をモデル化するための大規模な並列監督を必要としますが、完全に並列した現実世界のカップルは収集し、合成データには十分な現実主義と多様性が欠如します。 この課題に対処するために、我々は、UniVOEを紹介し、ビ

Machine translation (Meta M2M100); the English original follows.

Abstract

Video object removal and injection are reciprocal editing operations between object-present and object-absent video states, yet they differ substantially in training difficulty. Removal can be effectively bootstrapped from video inpainting and further adapted to erase object-related interaction effects. Injection, however, requires large-scale paired supervision to model object appearance, dynamics, and scene-dependent effects, while perfectly aligned real-world pairs are difficult to collect and synthetic data lacks sufficient realism and diversity. To address this challenge, we present UniVOE, a removal-driven framework for training both video object removal and injection models. We first train an effect-aware remover by transferring video inpainting priors and finetuning on object-present/object-absent pairs. The trained remover converts real object-present videos into object-absent counterparts, creating large-scale paired supervision for injection. An injection model is trained on these pairs in the reverse direction to recover objects together with their dynamics and interaction effects. Moreover, since pixel-level reconstruction alone cannot adequately capture perceptual realism, we introduce a reward-based post-training method that aligns injection results with human preferences using a proprietary reward model. Experiments against strong open-source and commercial systems demonstrate that UniVOE achieves state-of-the-art performance on both object removal and injection, particularly in challenging interaction-effect cases.

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Topics: Inpainting, Computer science, Object (grammar), Artificial intelligence, Computer vision, Video editing

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange