{"id":"d39070a6-1711-4c24-b100-64c2954df130","arxiv_id":"2501.10018","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":4,"one_line_summary":"DiffuEraser is a stable-diffusion video inpainting model that injects ProPainter priors via DDIM inversion and expands temporal receptive fields for long-sequence consistency.","lead":"DiffuEraser uses a diffusion model with an injected prior to fill masked regions in video, aiming for sharper textures and better temporal consistency than transformer-based methods like ProPainter. The paper reports qualitative gains on large masks and long sequences, but provides no quantitative metrics to back the claim.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The central claim of outperforming SOTA video inpainting is supported only by qualitative figures against a single baseline, with no quantitative metrics, ablations, or evaluation protocol; the claimed superiority is not established.","rationale":"The reader's stated weakest assumption is the technical validity of DDIM inversion as a prior (Section 3.2). My load-bearing concern is broader and more fundamental: the paper provides no quantitative evidence whatsoever for the central claim of state-of-the-art performance. This concern subsumes the technical assumption because even a perfect prior would not substantiate 'outperforms' without a comparative evaluation. However, the reader's overall rationale explicitly identifies the same evidence gap ('no quantitative metrics, ablations, or evaluation protocol'), so we partially agree. The reader's verdict REJECT remains appropriate: the central empirical claim is unsupported, and no amount of architectural plausibility can replace missing measurements. I do not see an internally inconsistent derivation or a clear technical flaw in the method itself, but the evidence presented is insufficient to accept the claimed superiority.","tokens_in":132,"tokens_out":1873,"duration_ms":31658,"concrete_test":"Run DiffuEraser and ProPainter (and ideally AVID, FloED, and BIVDiff) on a standard video-inpainting benchmark such as DAVIS 2017 with publicly available mask sequences, and compute region-masked PSNR, SSIM, LPIPS, and a temporal consistency metric (e.g., warp error or VFID) across the test set. Then perform a paired significance test (e.g., Wilcoxon signed-rank) over clips. If DiffuEraser does not significantly beat ProPainter on these metrics, the headline claim is unsupported.","verdict_should_be":"UNCHANGED","load_bearing_attack":"Section 4 ('Experiments') provides training details, a single efficiency number, and qualitative side-by-side comparisons against ProPainter only (Figures 1, 10–13). No PSNR, SSIM, LPIPS, VFID, warp error, user study, or significance test is reported, and no comparison is made with other diffusion-based inpainting methods (e.g., BIVDiff, AVID, FloED, FFF-VDI). The paper's strongest claim—'outperforms state-of-the-art techniques in both content completeness and temporal consistency'—is an empirical superiority claim, so its validity depends on measured improvements under a reproducible protocol. The report never provides such measurements, making the claim unfalsifiable from the submitted evidence. Additionally, the assertion in Section 3.2 that 'any blur and mosaic artifacts present in the prior do not adversely affect our model's outputs' is an empirical claim with no supporting data. Even if the DDIM-inversion prior is internally sound, the central claim still lacks the quantitative demonstration required to support it. The absence of ablations for the three proposed components (prior injection, expanded temporal receptive field, staggered denoising) further prevents attribution of any observed qualitative gains to the novel mechanisms, so the paper does not establish that its specific contributions cause the claimed improvement.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes DiffuEraser, a video inpainting model built on a Stable Diffusion backbone, combining a BrushNet-style masked-image conditioning branch with a motion module and temporal attention for video generation. To improve long-sequence consistency and reduce diffusion hallucinations, the method injects priors by applying DDIM inversion to the output of a transformer-based prior model (ProPainter) and using the inverted latent as initialization and weak conditioning. It also expands the temporal receptive field via pre-propagation/pre-inference and uses a staggered denoising schedule to smooth clip boundaries. The authors claim their method outperforms state-of-the-art video inpainting techniques in content completeness and temporal consistency while maintaining acceptable efficiency. The experimental section, however, reports only qualitative comparisons against ProPainter on a few illustrative videos, with training details and a single efficiency number, and no quantitative metrics, ablations, or evaluation protocol.","tokens_in":9034,"tokens_out":3495,"duration_ms":35007,"significance":"If the claimed improvements are validated, DiffuEraser would be a practically useful contribution: it combines the generative strength of diffusion models with the known-pixel propagation of an optical-flow based prior, and it addresses long-sequence temporal consistency with inference-time techniques. The paper provides a public code repository, detailed training settings, and an efficiency measurement (about 200 seconds for a 10-second 540p clip on an L20 GPU), which are useful starting points for reproducibility. However, the absence of any quantitative evaluation against competing methods, and the lack of ablations for the three proposed mechanisms, means the central claim of state-of-the-art performance is not currently established. The significance would be much higher if the paper included standard metrics (PSNR, SSIM, LPIPS, VFID, warp error, user study) and comparisons with existing diffusion-based video inpainting methods.","major_comments":[{"comment":"The central claim, stated in the abstract and conclusion, that DiffuEraser 'outperforms state-of-the-art techniques in both content completeness and temporal consistency' is not supported by any quantitative evidence. The only comparison is qualitative and against a single baseline, ProPainter (Figures 1, 10-13). No PSNR, SSIM, LPIPS, VFID, warp error, user study, or significance test is reported, and no evaluation protocol or test dataset is described. This makes the empirical superiority claim unfalsifiable from the submitted material. The authors should add a thorough quantitative evaluation on standard video inpainting benchmarks, comparing against several state-of-the-art methods, including diffusion-based baselines such as BIVDiff, AVID, FloED, and FFF-VDI, and report error bars or significance tests where applicable.","section":"Section 4 (Experiments)"},{"comment":"The paper asserts that 'any blur and mosaic artifacts present in the prior do not adversely affect our model's outputs; instead, they are refined and eliminated.' This is an empirical claim that is not backed by any experiment or ablation. The validity of the DDIM-inversion prior as an initialization for the diffusion denoiser is also not tested; if the inversion is inaccurate, the prior could introduce artifacts or bias the output. An ablation that removes the prior, uses a deliberately degraded prior, and measures output quality would be needed to support the claim and to justify the design choice of using ProPainter as the prior model.","section":"Section 3.2 (Incorporation of Priors)"},{"comment":"The proposed temporal-consistency optimizations—prior injection, expanded temporal receptive field via pre-propagation/pre-inference, and the staggered denoising schedule—are not individually ablated. Without per-component ablations, the observed qualitative improvements cannot be attributed to the specific mechanisms proposed. The authors should provide a component-wise ablation study with quantitative metrics on long sequences, including measuring temporal consistency at clip boundaries and across the full video.","section":"Section 3.3 (Temporal Consistency)"}],"minor_comments":[{"comment":"There is a typo: 'fist stage' should be 'first stage'.","section":"Section 4 (Training Details and Metrics)"},{"comment":"The name 'ProPainter' is written inconsistently as 'Propainter' and 'ProPainter'; please unify the spelling.","section":"Throughout"},{"comment":"The statement 'After experimental comparisons, we selected Propainter as our prior model' is not supported by any reported comparison. The authors should describe these comparisons or remove the claim.","section":"Section 3.2"},{"comment":"The efficiency measurement ('about 200 seconds' for a 10-second 540p clip) should specify the hardware, whether it includes the prior model inference and the pre-inference steps, and how it compares with the cost of ProPainter or other baselines.","section":"Section 4 (Efficiency)"},{"comment":"The related work lists several recent diffusion-based video inpainting methods, but the experiments compare only against ProPainter. At least one diffusion-based baseline should be included in the comparison to support the claim of improvement over state-of-the-art in the diffusion category as well.","section":"Related Work and Experiments"},{"comment":"The qualitative comparison in Figure 5 would be more informative if the same random seed and inference settings were specified, since diffusion models are stochastic; please state whether the results are cherry-picked or representative.","section":"Figure 5"}],"recommendation":"major_revision","confidential_remarks":"The manuscript is more of a technical report than a complete journal paper in its current form. The central empirical claim is unsupported, and the lack of any quantitative evaluation is likely to be seen as a fundamental deficiency by readers. That said, the proposed method is technically plausible, and the issues are addressable with additional experiments and ablations. If the authors can provide a rigorous evaluation, the paper could become a solid contribution. I would not reject outright because the missing evidence is within the scope of a major revision."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Quick take: this is a work-in-progress technical report. The system is a sensible engineering combination—BrushNet plus an AnimateDiff-style motion module, DDIM-inversion prior injection from ProPainter, and pre-inference/staggered denoising to smooth clip boundaries. That combination is genuinely new, and the authors are honest about the pieces being known; the contribution is integration and inference-time tricks. The ideas are reasonable and the write-up is clear. I believe them that this likely helps in practice.\n\nThe soft spot is not subtle: the paper claims to outperform state-of-the-art but reports no numbers. No PSNR/SSIM/LPIPS/VFID, no warp error, no user study, no comparison against other diffusion inpainting methods like BIVDiff, AVID, FloED, or FFF-VDI. The only evidence is a handful of side-by-side figures against ProPainter, with no protocol, no error bars, no ablations for the three proposed components. The claim in Sec. 3.2 that blur from the prior does not hurt is an empirical assertion with zero data. From the submitted evidence, the headline claim is unfalsifiable. That is a load-bearing flaw, not a minor omission.\n\nAlso, the experiments section gives training details but no evaluation split, no test dataset, no mask protocol for evaluation. Even the reproducibility is limited, though they provide a code link. The efficiency number (200 seconds for a 10-second 540p video on an L20) is a single point without runtime comparisons.\n\nWhat is good: the decomposition of video inpainting into known-pixel propagation, unknown-pixel generation, and temporal consistency is clean, and the proposed fixes map directly to those subproblems. The DDIM-inversion prior injection is a reasonable idea, and the staggered denoising at clip boundaries is creative. These are worth testing properly.\n\nVerdict: as a technical report, this is a useful pointer for someone building video inpainting systems, but it is not a demonstration. If the authors add a real evaluation suite—metrics on a standard benchmark or a clearly described custom one, ablations for the prior and the temporal tricks, and comparisons against other diffusion methods—the paper could become a solid contribution. Without that, I would not trust the claim.\n\nRecommendation: I would not send the current version to peer review; it is not referee-ready. If the authors commit to adding the missing quantitative evaluation and ablations, a serious editor could reasonably invite a revision, but as submitted this looks more like a preprint for industry sharing.","headline":"A plausible engineering combination for diffusion video inpainting, but the paper's central claim of outperforming SOTA is unsupported by any quantitative evaluation, ablations, or comparisons beyond a few qualitative figures against ProPainter.","tokens_in":9549,"tokens_out":2289,"would_cite":false,"duration_ms":22105,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"The paper claims that a stable-diffusion video inpainter, seeded with a DDIM-inverted prior from ProPainter and stabilized by staggered clip denoising, beats transformer baselines in detail and temporal consistency on large masks and long…","keywords":["video inpainting","diffusion model","temporal consistency","DDIM inversion","prior conditioning","long-sequence inference","motion module","stable diffusion"],"falsifier":"Run DiffuEraser on identical large-mask video clips in two configurations: with the DDIM-inverted ProPainter prior and with pure noise or a random prior latent. If the no-prior version matches or beats the prior version on a quantitative temporal-consistency metric (e.g., warped error) and FID, the paper's claim that injected priors suppress artifacts fails.","tokens_in":8579,"feed_emoji":"🎬","tokens_out":4281,"duration_ms":38734,"temperature":0.7,"pith_summary":"DiffuEraser tries to establish that a stable-diffusion-based video inpainting model can outperform the transformer-based ProPainter pipeline on large masks and long sequences by decomposing the task into three sub-problems: propagating known pixels, generating unknown pixels, and keeping completed content temporally consistent. It claims that injecting the DDIM-inverted output of a weaker prior model into the diffusion noise latent gives better initialization and weak conditioning, reducing artifacts and hallucinations. It also claims that expanding the temporal receptive field via pre-propagation and pre-inference, plus a staggered denoising schedule at clip boundaries, yields temporally stable long-video inpainting. If true, this would give practical video editing a way to combine flow-based propagation reliability with diffusion-generated detail.","feed_headline":"Diffusion model sharpens video inpainting and holds long clips steady","feed_subtitle":"An injected prior plus staggered denoising beats transformer baselines on big masks and long sequences.","key_machinery":"The central mechanism is prior injection: applying DDIM inversion to the prior model's inpainted frames and adding the inverted latent to the diffusion model's noisy input, which serves both as a better starting point for denoising and as a weak condition that suppresses hallucinated objects. The second mechanism is the staggered denoising schedule that alternates denoising starts between the clip start and the clip midpoint, exploiting the video diffusion model's inherent temporal smoothing to remove cliff-edge jumps between consecutive clips. The third is receptive-field expansion: pre-propagation for the prior model and pre-inference for DiffuEraser on sampled frames, so information from distant frames guides the final pass on every frame.","core_discovery":"On the paper's terms, DiffuEraser is a video inpainting model built from BrushNet with AnimateDiff-style motion modules, trained on Panda-70M clips with random masks. The key inference-time moves are: (1) run ProPainter first, apply DDIM inversion to its output, and add the inverted latent to the noisy latent as initialization and weak conditioning; (2) for long videos, run a pre-propagation and pre-inference on sampled frames to extend the temporal receptive field across the entire video, then guide frame-by-frame inference; (3) at clip intersections, use a staggered denoising schedule where even and odd timesteps start from the clip start and clip midpoint, exploiting the video diffusion model's temporal smoothing to blend boundaries. The paper reports that this outperforms state-of-the-art methods in both content completeness and temporal consistency while keeping inference practical via two-step Phased Consistency Model sampling, processing a 10-second 540p video in about 200 seconds on an L20 GPU.","pith_inferences":["The DDIM-inversion prior injection is a general recipe: any clip-based video diffusion edit task, such as object replacement or local stylization, could adopt the same 'run a cheap prior, invert it, add to the latent' strategy to stabilize outputs even if the prior itself is blurry.","The staggered denoising trick likely transfers to other video diffusion models that suffer from within-clip consistency but cross-clip jumps, since it only requires controlling the denoising start timestep per clip.","A testable extension: swap ProPainter for a weaker or faster prior and measure whether final quality stays similar; the paper's claim that prior artifacts get refined away predicts quality should be insensitive to prior blur, which can be quantified.","The long-sequence consistency claim rests on qualitative examples; a quantitative temporal-consistency metric over long videos, such as warped error or a learned temporal score, would give a sharper verification than the figures shown."],"forward_implications":["Inpainting results on large masks will contain more fine texture and fewer blur artifacts than transformer-based generation, because diffusion generation replaces the transformer's content-completion stage.","Temporal flicker at boundaries between clips in long-sequence inference will be reduced, making the method usable for full-video editing rather than only short clips.","The injected-prior technique should suppress the hallucinated objects that pure diffusion inpainting tends to introduce, improving fidelity in object-removal scenarios.","By using Phased Consistency Model sampling, the method stays practical: a 10-second 540p video is processed in about 200 seconds on a single L20 GPU."],"supporting_citations":[{"why":"Supplies the BrushNet dual-branch image inpainting architecture that DiffuEraser extends with motion modules.","marker":"[16]"},{"why":"Supplies the AnimateDiff motion module inserted into the denoising UNet for temporal modeling.","marker":"[11]"},{"why":"Gives the DDIM inversion procedure used to convert the prior model's output into a noisy latent initialization.","marker":"[33]"},{"why":"ProPainter is the selected prior model whose output is inverted and injected, and also the main state-of-the-art baseline compared against.","marker":"[46]"},{"why":"Provides the Phased Consistency Model two-step sampling that makes inference efficient.","marker":"[35]"},{"why":"Panda-70M is the training dataset from which video clips are sampled and random mask sequences are generated.","marker":"[2]"},{"why":"Cited as inspiration for interpolating between timesteps to obtain intermediate results, informing the staggered denoising approach.","marker":"[9]"}],"fun_headline_variants":["DiffuEraser: diffusion video inpainting beats transformers on big masks","Diffusion video inpainting holds long clips steady with better detail","Diffusion model fills video gaps without flicker, even for long scenes","Video inpainting gets a diffusion boost for temporal consistency","DiffuEraser diffusion model sharpens video inpainting on large masks"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The method assumes that running DDIM inversion on the prior model's (ProPainter's) output produces a noisy latent that correctly initializes the diffusion denoiser at every timestep; if that inverted latent is inaccurate, the prior could inject errors instead of removing them.","fun_headline_variants_meta":{"raw":{"variants":["DiffuEraser: diffusion video inpainting beats transformers on big masks","Diffusion video inpainting holds long clips steady with better detail","Diffusion model fills video gaps without flicker, even for long scenes","Video inpainting gets a diffusion boost for temporal consistency","DiffuEraser diffusion model sharpens video inpainting on large masks"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.001111,"raw_usage":{"total_tokens":4620,"prompt_tokens":931,"completion_tokens":3689,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":547,"completion_tokens_details":{"reasoning_tokens":3595}},"tokens_in":547,"tokens_out":3689,"duration_ms":27622,"temperature":1.0,"reasoning_tokens":3595,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-10T19:22:47.715935+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run DiffuEraser on identical large-mask video clips in two configurations: with the DDIM-inverted ProPainter prior and with pure noise or a random prior latent. If the no-prior version matches or beats the prior version on a quantitative temporal-consistency metric (e.g., warped error) and FID, the paper's claim that injected priors suppress artifacts fails.","supporting_citations":[{"cited_title":"Brushnet: A plug-and-play image inpainting model with decomposed dual-branch diffusion, 2024","cited_arxiv_id":null,"evidence_quote":"Supplies the BrushNet dual-branch image inpainting architecture that DiffuEraser extends with motion modules."},{"cited_title":"Animatediff: Animate your personalized text-to- image diffusion models without specific tuning","cited_arxiv_id":null,"evidence_quote":"Supplies the AnimateDiff motion module inserted into the denoising UNet for temporal modeling."},{"cited_title":"ProPainter: Improving propagation and transformer for video inpainting","cited_arxiv_id":null,"evidence_quote":"ProPainter is the selected prior model whose output is inverted and injected, and also the main state-of-the-art baseline compared against."},{"cited_title":"Panda-70m: Captioning 70m videos with multiple cross-modality teachers","cited_arxiv_id":null,"evidence_quote":"Panda-70M is the training dataset from which video clips are sampled and random mask sequences are generated."}],"review_version":1}