Removing Objects and Effects in Videos
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Key Takeaways
- Video object removal often fails to clear induced effects because existing methods rely on implicit correspondences, but EffectLearner introduces a dedicated reasoning mechanism.
- The system combines Wan2.2-TI2V-5B as the video generation backbone with Qwen2.5-VL-3B-Instruct as the vision-language model backbone.
- EffectLearner achieves an LPIPS score of 0.054 and an FVD score of 81.866 on ROSE-Bench.
- On EffectWorld-Wild, it obtains a Subject Consistency of 0.958, Background Consistency of 0.958, Motion Smoothness of 0.986, Dynamic Degree of 0.400, Imaging Quality of 0.557, and Total Score of 0.772.
- Limitations include over-smoothed textures when reconstructing occluded backgrounds and incorrect removal of thin structures during severe motion blur.
Summary & Methodology Analysis
Traditional video object removal techniques struggle because they eliminate target objects without properly addressing their induced effects, relying instead on implicit correspondences from fixed data distributions. EffectLearner solves this by introducing a dedicated reasoning mechanism powered by a vision-language model. The architecture adopts Wan2.2-TI2V-5B as the video generation backbone and Qwen2.5-VL-3B-Instruct as the vision-language model backbone, which is a multimodal neural network that processes both images and text. It constructs a target-highlighted video input to make target locations explicit and uses a structured effect-analysis prompt to guide the vision-language model in analyzing the target, motion, scene interactions, and induced effects. Compact effect-aware context tokens are extracted using learnable effect queries that interact with the hidden states of the vision-language model, representing internal layers of the network that encode intermediate representations.
Interactive System Flowchart
Cross-Examination & FAQs
A deeper dive clarifying mechanics, constraints, and baseline evaluations.
Q1. What is the main problem addressed by the paper?
Video object removal must eliminate not only the target object but also its induced effects while maintaining high-fidelity and spatiotemporally coherent restoration, yet existing methods mainly learn object-effect correspondences implicitly from predefined effect categories and fixed data distributions without a dedicated reasoning mechanism.
Q2. What core video generation backbone does the system use?
The system adopts Wan2.2-TI2V-5B as the video generation backbone.
Q3. What vision-language model backbone is employed?
It adopts Qwen2.5-VL-3B-Instruct as the vision-language model backbone.
Q4. How does the system make the target location explicit in the input?
It constructs a target-highlighted video input to make the target location explicit.
Q5. How are effect-aware context tokens extracted?
It extracts compact effect-aware context tokens using learnable effect queries interacting with vision-language model hidden states.
Q6. What visual conditions are provided to the diffusion transformer?
It concatenates the source video latent and the downsampled object mask with the noisy target latent as low-level visual conditions for the diffusion transformer.
Q7. What objective function is applied during training?
It applies a region-weighted flow-matching objective using an object mask and an effect mask.
Q8. What performance did EffectLearner achieve on ROSE-Bench?
On ROSE-Bench, EffectLearner achieves an LPIPS score of 0.054 and an FVD score of 81.866.
Q9. What are the primary limitations reported by the paper?
Reconstructing high-frequency background content occluded by large targets can result in over-smoothed textures and local distortions, and severe motion blur and repeated occlusion can cause the model to incorrectly remove parts of thin structures that should have been preserved.