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REVIEW 3 major objections 4 minor 41 references

EffectLearner: World-Aware Object-Effect Reasoning for Real-World Video Object Removal

T0 review · 3 major / 4 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read A VLM-guided video eraser removes objects and their induced effects (shadows, reflections, ripples, smoke) and reports gains on self-built benchmarks with mixed external results on ROSE-Bench.

desk verdict A genuinely new VLM-conditioned eraser plus a useful UE dataset, but the headline benchmark shares its pipeline with training, so the SOTA claim rests on in-domain numbers. read the letter →

arxiv 2608.05565 v1 pith:TVHI54Z2 submitted 2026-08-06 cs.CV

Feier Wu , Wanke Xia , Xu He , Zilang Zhou , Si Chen , Dongxia Liu , Liyang Chen , Qimeng Wu
show 3 more authors
Zhengbo Zhang Wenming Yang Zhiyong Wu
This is my paper · ORCID
classification cs.CV
keywords videoobjectremovaleffectsobject-effectreal-worldcomplexeffectlearner
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

When you remove an object from a video, the job is not finished when the object itself is gone. A person walking in sunlight leaves a moving shadow; a boat leaves ripples and a wake; a figure near water has a reflection. A removal system must repaint all of these as well, so the scene looks as if the object was never there. EffectLearner works in two stages. First, a vision-language model, a large AI system trained on images, videos and text, watches the video with the target object highlighted in red with a yellow outline. A structured prompt asks it to identify the object, track its motion, and list every effect it causes: shadows, reflections, light changes, ripples, smoke, dust and motion trails. Its internal representations are compressed into a small set of context tokens. Second, a diffusion-based video generator repaints the object and those effects, guided by the context tokens, the original video and the mask. The authors also built a training dataset, EffectWorld, by rendering 3D scenes in Unreal Engine: each scene is rendered once with the object present and once with it hidden, producing aligned before/after pairs that include complex effects such as splashes and smoke. Extra losses keep the edits consistent across frames. On the authors' own test sets the method clearly beats previous systems. On the external ROSE-Bench benchmark it wins on perceptual quality (LPIPS) and temporal coherence (FVD) but loses to the ROSE method on three pixel-level metrics (PSNR, SSIM, MAE). The largest gains come from a benchmark built with the same rendering pipeline as the training data, so how much of the advantage transfers to truly open-world video is not yet settled.
Extended reading notes

Core claim

The paper's central claim (Abstract and Sec. 1): EffectLearner, combining a VLM-based Object-Effect Reasoner with a DiT-based Video Eraser plus motion-aware stabilization, jointly removes the target and its induced effects in complex real-world scenes, delivering 'state-of-the-art removal quality, stronger robustness to complex effects, and improved spatiotemporal stability.' If true, open-vocabulary semantic reasoning about object-scene interactions removes the dependence on predefined effect categories and fixed distributions that limits ROSE, EffectErase, and similar methods.

Load-bearing premise

The paired-training premise (Algorithm 1, Supp. C; Sec. 3.5): re-rendering the same UE LevelSequence with the target actor hidden produces ground-truth videos in which the target and all (and only) its causal effects are removed, and EffectWorld-Eval is drawn from this same pipeline. If the renderer misses indirect or contact lighting effects, or removes non-causal appearance changes, both the training signal and the headline benchmark inherit that bias. The method's largest measured margins (PSNR 29.52 on EffectWorld-Eval vs 25.96 for ROSE) then partly reflect a shared synthetic distribution rather than true open-world generalization.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper proposes EffectLearner, a video object removal framework that combines a VLM-based Object-Effect Reasoner with a DiT-based Video Eraser, augmented by motion-aware mask guidance and a motion-consistency loss. The authors also construct EffectWorld, a paired Unreal Engine rendered dataset of complex object-effect scenarios, and introduce a progressive training curriculum. The main claims are state-of-the-art removal quality, robustness to complex induced effects, and open-world generalization, supported by experiments on ROSE-Bench, the newly introduced EffectWorld-Eval, and the unpaired EffectWorld-Wild benchmark.

Significance. If the generalization claim holds, the work would be a meaningful step beyond methods that rely on predefined effect categories: the VLM-based reasoner is a principled way to condition removal on semantic object-scene interactions, and the motion-stabilization components address a real temporal-consistency gap. The supplement is unusually transparent, including full prompts, construction algorithms, evaluation protocols, and failure cases, and the ablation study (Table 3) isolates each component's contribution. However, the significance is substantially tempered by the fact that the headline margins are measured on a benchmark generated by the same UE pipeline and distribution as the training set, while the external evidence for open-world superiority is mixed or not statistically supported.

major comments (3)
  1. [Sec. 4.1 / Table 1 / Supp. A.2] The paper does not establish that EffectWorld-Eval is distributionally disjoint from the EffectWorld training set. Training uses 11,048 EffectWorld triplets generated by the pipeline in Sec. 3.5 and Algorithm 1, and EffectWorld-Eval is described only by effect-label composition (Supp. A.2), with no statement about disjoint UE scenes, environment layouts, target assets, or camera/object trajectory configurations. The large Table 1 margins (e.g., PSNR 29.521 vs. 25.955 and FVD 69.184 vs. 887.541 against ROSE) are therefore compatible with in-domain fitting to the hidden-actor rendering mode rather than with semantic open-world generalization. The authors should document explicit disjointness or, preferably, evaluate on a held-out benchmark generated from a different distribution.
  2. [Sec. 4.2 / Table 2 / Supp. E] The external evidence does not support the strong 'clear advantages' claim. On ROSE-Bench, EffectLearner trails ROSE on PSNR, SSIM, and MAE; on EffectWorld-Wild, the VBench Total is tied with VACE at 0.772, and the human/LLM evaluations (Tables IV and V) are unpaired, lack error bars, and no significance tests are reported. These results do not establish open-world superiority over existing methods. The authors should provide paired statistical comparisons and, ideally, removal-specific metrics on the unpaired real-world set, since VBench measures video-generation quality rather than removal completeness or effect erasure.
  3. [Supp. C / Algorithm 1] The paired training target is generated by hiding the target actor and re-rendering the same LevelSequence. It is not verified that this hidden-actor rendering removes all causally induced effects (e.g., indirect lighting, contact shadows, caustics) or that it does not remove non-causal appearance changes. If the renderer under- or over-removes effects, both the training signal and the EffectWorld-Eval ground truth inherit the same bias. A concrete validation step, such as comparing hidden-actor renders against manually specified effect-region ground truths on a sample of scenes, would be needed to rule out this correctness risk.
minor comments (4)
  1. [Figure 3 / Sec. 4.3] The phrase 'weakly correlated object–effect d' appears to be truncated; it should read 'object-effect dependencies' or similar.
  2. [Abstract / Sec. 4.2] The abstract states that EffectLearner 'outperforms existing baselines on most metrics' on ROSE-Bench, but Table 1 shows ROSE is better on PSNR, SSIM, and MAE. The claim should be rephrased to 'competitive on standard fidelity metrics with best perceptual and temporal metrics'.
  3. [Sec. 4.2 / Table 2] The text says EffectLearner 'achieves the best total score' on EffectWorld-Wild, but the VBench Total is tied with VACE at 0.772; please clarify the tie or the criterion used to break it.
  4. [Supp. E.2] The reported LLM-human correlation of r = 0.9837 would be more convincing with details on the number of ratings, the variance across samples, and a confidence interval.

Circularity Check

1 steps flagged · score 4.0 of 10

EffectWorld-Eval shares the UE training pipeline with no documented train/eval disjointness, so the headline Table 1 margins are in-distribution evidence; the VLM-to-DiT derivation itself is self-contained.

  1. other [Sec. 4.1 (Benchmarks) and Table 1; Sec. 3.5 (curriculum); Supp. A.2 vs Supp. B/C (Algorithm 1)]
    "EffectWorld-Eval is a paired benchmark containing 33 complex-effect videos, each with a source video, an object mask, and an object-removed target. Its composition follows the main challenges studied in this work: 29 videos contain compositional effects, 8 exhibit spatially detached or weakly correlated object-effect relations, and all 33 involve object motion."

    The headline evidence for 'clear advantages' on EffectWorld-Eval (Table 1: PSNR 29.52 vs 25.96) is gathered on a benchmark generated by the same UE pipeline (Algorithm 1, Supp. C: random camera and target trajectories) as the EffectWorld training split (11,048 of the same 11,092-triplet collection, Supp. B). No passage documents disjointness of scenes, actors, or trajectories between the 33 EffectWorld-Eval videos and the EffectWorld training triplets. The curriculum explicitly trains on the eval's challenge mix: 'We then increase the proportion of compositional, dynamic, and spatially detached samples' (Sec. 3.5), the eval's defining categories, so the eval re-measures the training distribution.

full rationale

The central derivation is self-contained in the equation sense. The VLM-Based Object-Effect Reasoner (Sec. 3.2) consumes the target-highlighted video and a structured prompt and produces context tokens via learned effect queries; the DiT-Based Video Eraser (Sec. 3.3) is conditioned on those tokens plus source and mask latents and trained with the flow-matching objective (Eqs. 1, 2, 6, 9). No training quantity is defined in terms of the test output, and no fitted parameter is renamed as a prediction. External anchors are genuine: ROSE-Bench is 'the external paired benchmark' (Supp. A.1) from another group, and EffectWorld-Wild uses real, unpaired Pexels/DAVIS videos scored with the external VBench suite and with a transparently specified human/LLM protocol (Supp. E; r = 0.9837 human-LLM agreement). The supplement's documented failure cases (Supp. F) further show the system has non-circular, real failure modes. The one substantive circularity is the self-built EffectWorld-Eval: it is generated by the same Algorithm 1 pipeline and the same challenge composition as the EffectWorld training split, with no documented disjointness, and with a curriculum that explicitly up-weights exactly the eval's defining sample types. The large Table 1 margins on EffectWorld-Eval (PSNR 29.52 vs 25.96; FVD 69.2 vs 887.5) are therefore compatible with in-domain fitting to the hidden-actor rendering mode rather than with the claimed open-world generalization (Sec. 5). This is a partial, benchmark-level circularity that weakens the headline generalization argument, but it does not make the framework's derivation equivalent to its inputs, so the score is 4 rather than 6 or higher.

Assumptions & free parameters 7 free parameters · 6 assumptions · 2 invented entities

No physical constants or fitted mathematical parameters are introduced; the free parameters are hand-set training weights, thresholds, and UE scene-sampling distributions. The load-bearing domain assumptions are the validity of the UE hide-and-re-render ground truth, the accuracy of the frozen VLM's effect analysis (never evaluated in isolation), the sufficiency of translational pseudo-flow for temporal consistency, and the validity of VBench no-reference metrics for open-world effect removal. The paper's new constructs (context tokens, pseudo-flow) are learned or algorithmic components with only internal ablative evidence, so the graviton problem does not apply.

free parameters (7)
  • effect mask threshold tau = 0.06 (latent channel-averaged |x0 - xs|)
    Supp. D.2 defines the effect region E by thresholding the latent difference at 0.06; this hand-set value determines which pixels receive the 0.30 semantic-loss weight.
  • region weights W = 4 (removal region), 2 (elsewhere)
    Supp. D.2, Eq. 11: W = 4 M_loss + 2 (1 - M_loss); hand-set amplification of the object/effect areas in the flow-matching loss.
  • motion consistency weight lambda_motion = 0.20
    Eq. 9 and Supp. D.2; chosen without a reported sensitivity study, yet the ablation (Table 3) shows removing it is the single largest degradation.
  • semantic effect loss weights = 0.30 (inside E), 0.20 (outside E)
    Supp. D.2, Eq. 12; hand-set coefficients for the latent regression losses.
  • highlight blend coefficient alpha and colors = alpha=0.15, red fill (255,0,0), yellow boundary (255,255,0)
    Algorithm 2 and Eq. 3; chosen by hand to make the target explicit without hiding appearance.
  • UE sampling ranges for camera/object motion = s_m ~ U(0.1,1.0), displacement U(-150 s_m, 150 s_m), yaw up to 360 deg, azimuth change <= 0.2 rad, polar change <= 0.1…
    Supp. C; hand-designed distributions controlling the synthetic data distribution, and hence the distribution of the EffectWorld-Eval benchmark too.
  • query/context token counts N_q, N_c = not reported
    Sec. 3.2 fixes the number and dimension of learnable effect queries and context tokens but the values are not stated, so the conditioning capacity is an unreportable hyperparameter.
assumptions (6)
  • domain assumption A UE render with the target actor hidden is a correct ground-truth 'object and effects removed' video
    Algorithm 1 (Supp. C) produces 02_Target by hiding the actor and attached actors; all training supervision and the EffectWorld-Eval benchmark assume this re-render removes exactly the target and its causal effects (shadows, reflections, lighting, traces).
  • domain assumption The frozen Qwen2.5-VL-3B reasoner identifies effects accurately from the highlighted video
    Sec. 3.2: the VLM is frozen, only the query connector is trained; no direct accuracy measurement of the reasoner's effect analysis is provided, so a hallucinated or missed effect propagates into the context tokens.
  • ad hoc to paper Mask-centroid displacement is a sufficient motion model for temporal consistency
    Algorithm 4 (Supp. D.5): the pseudo-flow is the negative centroid displacement broadcast over the latent grid; rotation, scale, occlusion and non-rigid motion are ignored.
  • domain assumption VBench no-reference dimensions proxy removal quality in the wild
    Sec. 4.1: EffectWorld-Wild has no paired ground truth, so conclusions rely on VBench, which measures generic video quality (dynamics, imaging quality), not whether the specific object and its effects were removed.
  • standard math Latent flow-matching formulation (Eqs. 1-2) is a valid training objective
    Sec. 3.1: the standard flow-matching objective is taken from the Wan2.2 backbone and prior diffusion literature; it is not in question.
  • domain assumption Temporal VAE compression loses short-lived mask positions, justifying the union mask
    Sec. 3.4: the claim that frame-level masks are insufficient under fast motion rests on the causal 3D VAE's temporal compression; the ablation (w/o M_union) is the only supporting evidence.
invented entities (2)
  • Effect-aware context tokens C_effect
    purpose: Compact semantic conditioning distilled from frozen VLM hidden states via learnable queries and a connector, replacing text conditions in the DiT cross-attention
    An internal learned representation with only ablative evidence (Table 3, w/o VLM: 27.74 vs 29.52 PSNR); no external falsifiable handle, unlike a physically postulated entity.
  • Translational pseudo-flow f_k from mask centroids
    purpose: Backward-warping motion model for the motion-consistency loss between neighboring latent frames
    A deliberate approximation stated in Supp. D.5; its adequacy is only measured through the motion-loss ablation, not against real optical flow.

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Cite this review

Pith. "Pith review of EffectLearner: World-Aware Object-Effect Reasoning for Real-World Video Object Removal." pith.science (2026). https://pith.science/paper/TVHI54Z2

@misc{pith2026260805565,
  author       = {Pith},
  title        = {Pith review of: EffectLearner: World-Aware Object-Effect Reasoning for Real-World Video Object Removal},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TVHI54Z2}},
  note         = {Machine review of arXiv:2608.05565}
}
read the original 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.

Figures

Figures reproduced from arXiv: 2608.05565 by the authors.

Figure 1
Figure 1. Overview of EffectLearner. The framework consists of two core components: (1) the VLM-Based Object-Effect Reasoner (left), which performs cross-modal reasoning over a target-highlighted input video under structured textual guidance to extract compact semantic context; and (2) the DiT-Based Video Eraser (middle), which integrates the semantic context with the source video and object mask to achieve high-fidelity obje… view at source ↗
Figure 2
Figure 2. Data construction pipeline of EffectWorld. dimension and fed into the DiT as low-level visual condi￾tions. The source latent preserves the appearance, texture, structure, and temporal information of the input video, while the mask specifies the target region for removal. Meanwhile, the effect-aware context tokens Ceffect replace the original text condition in the DiT cross-attention layers, providing high-level sema… view at source ↗
Figure 3
Figure 3. Visualization results of various methods on [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

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Works this paper leans on

41 extracted references · 20 canonical work pages

  1. [1]

    Advances in Neural Information Processing Systems , volume=

    Rose: Remove objects with side effects in videos , author=. Advances in Neural Information Processing Systems , volume=

    work page

  2. [2]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    Omni-effects: Unified and spatially-controllable visual effects generation , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    work page

  3. [3]

    MoZoo:Unleashing Video Diffusion power in animal fur and muscle simulation

    MoZoo: Unleashing Video Diffusion power in animal fur and muscle simulation , author=. arXiv preprint arXiv:2605.13857 , year=

    work page Pith review arXiv

  4. [4]

    IEEE Transactions on Visualization and Computer Graphics , year=

    Vidcraft3: Camera, object, and lighting control for image-to-video generation , author=. IEEE Transactions on Visualization and Computer Graphics , year=

    work page

  5. [5]

    Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

    Realcam-i2v: Real-world image-to-video generation with interactive complex camera control , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

    work page

  6. [6]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    Dreamphysics: Learning physics-based 3d dynamics with video diffusion priors , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    work page

  7. [7]

    European Conference on Computer Vision , pages=

    Physdreamer: Physics-based interaction with 3d objects via video generation , author=. European Conference on Computer Vision , pages=. 2024 , organization=

    2024

  8. [8]

    2026 , howpublished =

    Introducing. 2026 , howpublished =

    2026

Show all 41 references
  1. [9]

    arXiv preprint arXiv:2602.15763 , year=

    Glm-5: from vibe coding to agentic engineering , author=. arXiv preprint arXiv:2602.15763 , year=

    arXiv

  2. [10]

    arXiv preprint arXiv:2511.21631 , year=

    Qwen3-vl technical report , author=. arXiv preprint arXiv:2511.21631 , year=

    arXiv

  3. [11]

    arXiv preprint arXiv:2504.07491 , year=

    Kimi-vl technical report , author=. arXiv preprint arXiv:2504.07491 , year=

    arXiv

  4. [12]

    Applied Sciences , volume=

    Barriers to industry adoption of AI video generation tools: A study based on the perspectives of video production professionals in China , author=. Applied Sciences , volume=. 2024 , publisher=

    2024

  5. [13]

    Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

    A benchmark dataset and evaluation methodology for video object segmentation , author=. Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

  6. [14]

    2020 , howpublished =

    2020

  7. [15]

    2014 , howpublished =

    Free Stock Videos Shared by the Pexels Community , author =. 2014 , howpublished =

    2014

  8. [16]

    Proceedings of the IEEE/CVF international conference on computer vision , pages=

    Fuseformer: Fusing fine-grained information in transformers for video inpainting , author=. Proceedings of the IEEE/CVF international conference on computer vision , pages=

  9. [17]

    European conference on computer vision , pages=

    Learning joint spatial-temporal transformations for video inpainting , author=. European conference on computer vision , pages=. 2020 , organization=

    2020

  10. [18]

    Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

    Deep video inpainting , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

  11. [19]

    Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops , pages=

    Vornet: Spatio-temporally consistent video inpainting for object removal , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops , pages=

  12. [20]

    SSIM , author=

    Image quality metrics: PSNR vs. SSIM , author=. 2010 20th international conference on pattern recognition , pages=. 2010 , organization=

    2010

  13. [21]

    IEEE transactions on image processing , volume=

    Image quality assessment: from error visibility to structural similarity , author=. IEEE transactions on image processing , volume=. 2004 , publisher=

    2004

  14. [22]

    arXiv preprint arXiv:2603.02175 , year=

    Kiwi-edit: Versatile video editing via instruction and reference guidance , author=. arXiv preprint arXiv:2603.02175 , year=

    arXiv

  15. [23]

    arXiv preprint arXiv:2605.30045 , year=

    GenEraser: Generalizable Video Object Removal via Balanced Text-Mask Guidance and Decoupled Locator-Preserver , author=. arXiv preprint arXiv:2605.30045 , year=

    arXiv

  16. [24]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

    Vbench: Comprehensive benchmark suite for video generative models , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

  17. [25]

    Proceedings of the IEEE/CVF international conference on computer vision , pages=

    Propainter: Improving propagation and transformer for video inpainting , author=. Proceedings of the IEEE/CVF international conference on computer vision , pages=

  18. [26]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

    Viva: Vlm-guided instruction-based video editing with reward optimization , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

  19. [27]

    Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=

    RACCOON: Versatile Instructional Video Editing with Auto-Generated Narratives , author=. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=

    2025

  20. [28]

    Qwen2.5: A Party of Foundation Models , url =

    Qwen Team , month =. Qwen2.5: A Party of Foundation Models , url =

  21. [29]

    arXiv preprint arXiv:2503.20314 , year=

    Wan: Open and Advanced Large-Scale Video Generative Models , author=. arXiv preprint arXiv:2503.20314 , year=

    arXiv

  22. [30]

    Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

    Vace: All-in-one video creation and editing , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

  23. [31]

    arXiv preprint arXiv:2501.10018 , year=

    Diffueraser: A diffusion model for video inpainting , author=. arXiv preprint arXiv:2501.10018 , year=

    arXiv

  24. [32]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

    Effecterase: Joint video object removal and insertion for high-quality effect erasing , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

  25. [33]

    arXiv preprint arXiv:2604.02296 , year=

    Void: Video object and interaction deletion , author=. arXiv preprint arXiv:2604.02296 , year=

  26. [34]

    arXiv preprint arXiv:2603.09283 , year=

    From Ideal to Real: Stable Video Object Removal under Imperfect Conditions , author=. arXiv preprint arXiv:2603.09283 , year=

    arXiv

  27. [35]

    arXiv preprint arXiv:2601.06391 , year=

    Object-wiper: Training-free object and associated effect removal in videos , author=. arXiv preprint arXiv:2601.06391 , year=

  28. [36]

    arXiv preprint arXiv:2512.04515 , year=

    EgoLCD: Egocentric Video Generation with Long Context Diffusion , author=. arXiv preprint arXiv:2512.04515 , year=

  29. [37]

    arXiv preprint arXiv:2412.03603 , year=

    Hunyuanvideo: A systematic framework for large video generative models , author=. arXiv preprint arXiv:2412.03603 , year=

    arXiv

  30. [38]

    Proceedings of the IEEE/CVF international conference on computer vision , pages=

    Scalable diffusion models with transformers , author=. Proceedings of the IEEE/CVF international conference on computer vision , pages=

  31. [39]

    Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

    Towards an end-to-end framework for flow-guided video inpainting , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

  32. [40]

    Proceedings of the Computer Vision and Pattern Recognition Conference , pages=

    HomoGen: Enhanced Video Inpainting via Homography Propagation and Diffusion , author=. Proceedings of the Computer Vision and Pattern Recognition Conference , pages=

  33. [41]

    Enterprise Information Systems , volume=

    Recent advances in artificial intelligence for video production system , author=. Enterprise Information Systems , volume=. 2023 , publisher=

    2023

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Reviewed August 8, 2026 · model on record in the stance chip above.