EffectLearner: World-Aware Object-Effect Reasoning for Real-World Video Object Removal
- URL: http://arxiv.org/abs/2608.05565v1
- Date: Thu, 06 Aug 2026 03:34:19 GMT
- Title: EffectLearner: World-Aware Object-Effect Reasoning for Real-World Video Object Removal
- Abstract summary: 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.
- Score: 43.705438947753656
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Video object removal must eliminate not only the target object but also its induced effects while maintaining high-fidelity and spatiotemporally coherent restoration. Existing methods mainly learn object-effect correspondences implicitly from predefined effect categories and fixed data distributions, limiting their generalization to complex real-world scenes involving compositional effects, spatially detached or weakly correlated effects, long-tail physical phenomena, and dynamically evolving interactions. We propose EffectLearner, a semantic-reasoning-enhanced framework that combines a VLM-based Object-Effect Reasoner with a DiT-based Video Eraser. Guided by a structured effect-analysis prompt, the Reasoner performs cross-modal reasoning over a target-highlighted video and extracts compact effect-aware context, which guides the Video Eraser toward comprehensive object-effect removal. Motion-aware mask guidance and motion-consistency supervision further improve removal coverage and spatiotemporal stability under object motion and evolving scene dynamics. To fully exploit the framework in challenging real-world scenarios, we further construct EffectWorld, a paired video dataset specifically designed for complex object-induced effects, and introduce a progressive training curriculum that combines common supervision with complex-effect data. On the standard ROSE-Bench, EffectLearner outperforms existing baselines on most metrics and achieves clear advantages on both EffectWorld-Eval and the challenging EffectWorld-Wild, demonstrating its ability to deliver high-quality video object removal in complex real-world scenes.
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