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REVIEW 4 major objections 7 minor 6 cited by

MiniMax-Remover: Taming Bad Noise Helps Video Object Removal

T0 review · 4 major / 7 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Training against adversarial 'bad noise' produces a video object remover that runs in six sampling steps and needs no classifier-free guidance.

desk verdict Solid two-stage engineering with a genuinely interesting bad-noise distillation trick, but the SOTA claim is carried by an uncalibrated GPT-O3 judge, so read the success numbers as provisional. read the letter →

arxiv 2505.24873 v1 pith:7K2JO5BO submitted 2025-05-30 cs.CV

Bojia Zi , Weixuan Peng , Xianbiao Qi , Jianan Wang , Shihao Zhao , Rong Xiao , Kam-Fai Wong This is my paper
classification cs.CV
keywords videoobjectremovalinpaintingdiffusionmodelsflowmatchingminimaxoptimizationadversarialnoiseclassifier-freeguidancecontrastiveconditioning
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

The paper sets out to show that video object removal can be made simultaneously faster and more reliable by simplifying the generative model and then training it against inputs designed to make it fail. Instead of text prompts and cross-attention, the Stage-1 model uses two learnable contrastive tokens injected through self-attention, so the only user input is the mask. In Stage-2, a minimax objective searches for 'bad noise' that steers the model back toward the original, unremoved video, and then fine-tunes the model to remove objects even under that noise, on 10K human-approved examples produced by Stage-1. The claimed payoff is inference as short as six sampling steps with no classifier-free guidance, while reported quality metrics on standard benchmarks exceed the listed baselines, including a 91.11% object-removal success rate versus a previous best of 56.67% on DAVIS.

What carries the argument

The central mechanism is the minimax 'bad noise' loop carried out on a flow-matching DiT, a transformer-based diffusion network trained to predict velocity. The inner step fixes the timestep at $t=1.0$, where the noisy latent is pure noise $\epsilon$, and takes one gradient step of the velocity-prediction loss toward the original video as a bad target; the update $\epsilon^* \leftarrow \sqrt{1-\alpha}\,\epsilon - \sqrt{\alpha}\,\mathrm{sign}(\nabla_\epsilon)\cdot|\epsilon'|$ preserves near-Gaussian statistics while encoding object-related information. The outer step fine-tunes the network on those crafted noises so that its velocity prediction points toward a successful, human-approved removal. Supporting this is the Stage-1 conditioning design: positive and negative contrastive tokens are injected into the self-attention stream, replacing text and cross-attention and making the eventual removal of classifier-free guidance possible.

What would settle it

Run the Stage-2 training twice on the same 10K curated videos and identical hyperparameters, once with the adversarial 'bad noise' update and once with ordinary Gaussian noise; the paper's reported DAVIS success rates are 91.11% versus 72.22%, so a controlled replication that finds no significant gap would show the minimax step is not the source of the gain. A second check targets the transfer assumption: craft 'bad noise' at $t=1.0$ and then test whether inserting that same adversarial direction at intermediate timesteps also produces failure removals; if it does not, the single-step search does not span the real failure modes.

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Extended reading notes

Core claim

On its own terms, the paper claims that a pretrained video-generation DiT can be converted into a strong object remover by deleting text conditioning and cross-attention, injecting positive and negative condition tokens through self-attention, and then distilling the simplified model on human-curated successful removals with an adversarial minimax update. The inner maximization crafts noise that makes the model predict motion toward the original video, which is exactly the failure mode of object removal, while the outer minimization teaches the model to predict the curated successful removal from that same noise. Because the adversarial noise is constructed to stay near a Gaussian distribution, it remains a valid diffusion input during training, and the result is a model that works without classifier-free guidance and with as few as six sampling steps. The paper reports SSIM 0.9847, PSNR 36.66, temporal consistency 0.9776, visual quality 6.48, and a 91.11% success rate on DAVIS, all ahead of the compared baselines, with similar advantages on a 200-video Pexels set.

Load-bearing premise

The load-bearing premise is that the 'bad noise' found by one gradient step at the noisiest timestep, against the original video as a target, is representative of the noise patterns that cause failures during real multi-step inference; if that transfer fails, the Stage-2 robustness gains would disappear.

Editorial extensions

If this is right

  • Mask-only conditioning is enough: text prompts, optical flow, and DDIM inversion are not needed for competitive video object removal.
  • Six-step sampling without classifier-free guidance makes inference substantially cheaper than the listed 25-70-step guided baselines, at a small measured quality cost.
  • Training with adversarial 'bad noise' produces the largest Stage-2 improvement over random-noise training on the same curated data.
  • The quality gap between 6 and 50 sampling steps is small on DAVIS (SSIM 0.9842 versus 0.9847), so fast inference costs little fidelity.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The one-step search at $t=1.0$ may work because flow-matching targets at that timestep are dominated by noise, so crafted directions could perturb early denoising decisions; a direct test is whether the same 'bad noise' also causes failures when inserted at intermediate timesteps.
  • The minimax recipe is not unique to object removal: any mask-conditioned edit with an undesired 'regenerate the original' failure mode, such as object replacement or watermark removal, could use the same bad-noise construction.
  • The paper's own limitation note points to VAE encode/decode as the remaining inference bottleneck, so pairing the distilled six-step DiT with a cheaper autoencoder is the direct next lever for speed.
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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

4 major / 7 minor

Summary. The paper proposes MiniMax-Remover, a two-stage video object removal system. Stage 1 takes the Wan2.1-1.3B DiT generator and removes text-conditioning and cross-attention layers, replacing them with two learnable contrastive condition tokens (positive and negative) injected into the self-attention stream, while training on WebVid-10M with automatically generated masks. Stage 2 distills the Stage 1 model on 10K human-curated successful removal videos, using a 'minimax' scheme that searches for adversarial input noise ('bad noise') via a single gradient step at t=1.0 against a failure target, then fine-tunes the model to be robust to that noise. The resulting model uses 6 sampling steps without CFG. Experiments on DAVIS and 200 Pexels videos report SSIM 0.9847/0.9878, PSNR 36.66/36.98, and GPT-O3 success rates of 91.11%/81%, surpassing listed baselines, with about 0.18s latency per frame and 8.2GB GPU memory.

Significance. If substantiated, the contributions are practically significant: a lightweight 1.05B-parameter DiT remover with 6-step inference, no CFG, and no auxiliary priors (optical flow, text, DDIM inversion) would be a useful advance for video editing. The architecture simplification and the 'bad noise' training heuristic are interesting, and the ablation study in Table 3 consistently shows that the proposed components improve performance. The paper also includes a limitations section and reports concrete inference speed and memory figures. However, the central SOTA claim currently rests on an uncalibrated proprietary judge and on baseline comparisons that do not control frame length and resolution, so the reported margins are not yet persuasive. The theoretical justification of the minimax step is also not rigorous. The contribution is potentially publishable, but the empirical claims need stronger validation.

major comments (4)
  1. [Section 4, Table 2, Section 10] The headline success-rate claims are based entirely on GPT-O3 evaluations. No calibration against human raters is reported: the prompt tells the model the left image contains the target object highlighted by a blue mask, and the binary 'yes/no' success judgment is not validated on a subset with human labels. There are no error bars, no repetitions over decoding seeds or prompt variants, and no significance tests. More importantly, the baseline comparison is not controlled: the text states 'We set the evaluate frame length as 32. To evaluate with same frame length, we expand input frame length for VideoComposer and FloED. The rest video inpainters are used their default frame length of their code bases. The frame resolutions are used with their default resolutions.' Different frame lengths and resolutions materially affect temporal consistency and inpainting difficulty, so the margins in Table 2 may reflect testing conditions rather than removal quality. The user-preference result also omits participant count, participant selection, and the exact instructions. Please add a human evaluation with controlled frame lengths and resolutions, report variance or significance tests, and calibrate the GPT-O3 judge against human judgments on a subset.
  2. [Section 3.3.1 (Eq. 8-11) and Section 8.2 (Eq. 15)] The paper calls the inner step a 'maximization' but Eq. 9 actually “minimizes” the model's prediction error with respect to a failure target (the original video). This is a plausible heuristic for finding failure-inducing noise, but it is not a maximization of the loss in Eq. 8, and Eq. 15 in Section 8.2 does not establish that the constructed epsilon* is a worst-case noise or that robustness to it transfers to other timesteps. The claim that 'minimizing the loss on the “bad” input noise leads to improved performance on the clean input noise' is not supported by the presented mathematics. Since the empirical ablation (Table 3, Ab-2 vs. Ab-3) suggests the heuristic helps, please either reframe Stage 2 as adversarial data augmentation (with the ablation as evidence) or supply a rigorous analysis of the minimax objective.
  3. [Section 9.2, Figure 7] The claim that adversarial-based noise 'approximates a Gaussian distribution' is supported only by a single visual QQ-plot. This is load-bearing because the training procedure assumes epsilon* is compatible with the diffusion process's noise distribution. Please provide quantitative tests (e.g., Kolmogorov-Smirnov or chi-square statistics over multiple samples) and describe how the distribution changes with the alpha parameter. The update rule in Eq. 11 combines a Gaussian epsilon with sign(gradient)*|epsilon'|, which is not obviously Gaussian; the paper should explain why the resulting variable remains within the support of the diffusion noise distribution.
  4. [Section 4 (Inference Details) vs. Table 1] The latency and memory figures are inconsistent. Table 1 lists MiniMax-Remover as 0.18s latency and 8.2GB GPU memory, while the text reports 'the inference takes approximately 24 seconds per video' and 'around 14GB peak GPU memory (DiT for 8GB, VAE decoding for 6GB)' at 480p and 81 frames. Clarify whether Table 1 reports per-frame DiT-only latency and whether VAE decoding and the 81-frame context are included in the 0.18s figure. Also, Table 1 and Section 6 list Senorita-Remover as a related object-removal method, but it is absent from the quantitative comparisons in Table 2; since it is a closely related prior remover, its omission weakens the SOTA claim.
minor comments (7)
  1. [Abstract] The abstract says 'achieves a state-of-the-art video object removal results'; please correct the article and plural to 'achieves state-of-the-art video object removal results.'
  2. [Section 7 and Figure 5 caption] The text refers to 'position condition' where 'positive condition' is meant; the same typo appears in the Figure 5 caption.
  3. [Section 3.3] The spelling 'minmax' appears inconsistently with 'minimax' throughout; please unify the terminology.
  4. [Section 10] One of the two prompts says 'GPT-03' instead of 'GPT-O3'; please fix.
  5. [Section 4 (Training Details)] Training uses an input frame length of 81 while evaluation uses a frame length of 32; please add a sentence explaining the relationship and any sliding-window processing.
  6. [Table 3] The two sub-tables reuse the labels Ab-1 through Ab-4, which is confusing; please use distinct label sets for the Stage 1 and Stage 2 ablations.
  7. [Section 9.1] The discussion of inversion-based noise is brief; Table 5 shows inversion-based noise achieves 70.00 success vs. 72.22 for random noise, so the claim that inversion noise is 'unsuitable' would benefit from a more quantitative explanation of why it hurts performance.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: central claims are externally benchmarked; only minor self-citations in training details and metrics, none load-bearing.

full rationale

The derivation chain is not circular. Stage 2 trains against z_succ, which are Stage-1 outputs selected by human annotators; this is self-distillation, but the central claim (state-of-the-art removal) is tested on DAVIS and Pexels videos outside the training set and compared with published baselines, so the result is not defined in terms of the training targets. The adversarial 'bad noise' in Eqs. 9-11 is crafted from the model's own gradient, but the improvement is supported by the Ab-2 vs Ab-3 ablation (72.22% to 91.11% success), an external comparison under otherwise identical settings; it is not a fitted parameter renamed as a prediction. The self-citations (Senorita-Remover for mask selection, COCOCO for the TC metric) are not load-bearing: the mask strategy is fully described in Figure 5, and the temporal-consistency metric is standard practice. The GPT-O3 judge is proprietary and uncalibrated, which is a measurement-validity risk rather than a circularity, and the paper's evaluation is not equivalent to its inputs by construction. No equation in the paper reduces, by definition, to the quantity it claims to predict.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The paper introduces architectural components (contrastive condition tokens) and a training concept (bad noise), but these are learned model parameters and an optimization construction, not new physical or independent entities with falsifiable handles outside the paper. No new particles, forces, dimensions, or conserved quantities are postulated.

free parameters (5)
  • Stage 2 data mixture ratio = 1/3 curated adversarial data, 2/3 WebVid standard data
    The proportion is chosen by hand (Section 3.3.2) and affects how much the model sees adversarial vs clean data; no sweep is reported.
  • alpha in adversarial noise update = uniform in [0,1]
    Equation 11 uses a randomly sampled alpha; the distribution is chosen without analysis, and the Gaussian-closeness claim is only visually checked.
  • Number of gradient steps for bad-noise search = 1
    Section 3.3.1 searches with a single backpropagation; it is a design choice to limit compute, but no analysis shows one step is sufficient.
  • Number of condition tokens injected = 6
    Section 3.2.1 splits the condition embedding into 6 tokens; this hyperparameter is not varied in ablations.
  • CFG guidance scale w = not reported
    Stage 1 uses CFG with a manually tuned guidance weight; the value is never given, making reproduction harder.
assumptions (5)
  • domain assumption Wan2.1-1.3B is a valid pretrained flow-matching video generation model whose weights can be fine-tuned for inpainting.
    The entire method initializes from Wan2.1-1.3B [43] and assumes its learned video prior transfers to object removal (Section 3.2).
  • domain assumption Grounded-SAM2 and CogVLM2 generate accurate object masks and captions on WebVid-10M and Pexels.
    Stage 1 training data is built by automatically generating masks/captions; errors in these annotations would propagate into training (Section 4).
  • standard math Rectified Flow distillation is a sound method to reduce sampling steps from 50 to 6 without significant quality loss.
    The paper applies Rectified Flow [30] to distill the student model (Section 3.3).
  • domain assumption Human annotators can reliably identify successful object removals in the curated 10K set.
    Stage 2 relies on this curation as ground truth; no inter-annotator agreement is reported (Section 3.3).
  • domain assumption GPT-O3 scores and success-rate judgments are a valid proxy for visual quality and removal correctness.
    The main qualitative comparisons use GPT-O3 as judge with no calibration against human raters (Section 10).

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Cite this review

Pith. "Pith review of MiniMax-Remover: Taming Bad Noise Helps Video Object Removal." pith.science (2026). https://pith.science/paper/7K2JO5BO

@misc{pith2026250524873,
  author       = {Pith},
  title        = {Pith review of: MiniMax-Remover: Taming Bad Noise Helps Video Object Removal},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7K2JO5BO}},
  note         = {Machine review of arXiv:2505.24873}
}
read the original abstract

Recent advances in video diffusion models have driven rapid progress in video editing techniques. However, video object removal, a critical subtask of video editing, remains challenging due to issues such as hallucinated objects and visual artifacts. Furthermore, existing methods often rely on computationally expensive sampling procedures and classifier-free guidance (CFG), resulting in slow inference. To address these limitations, we propose MiniMax-Remover, a novel two-stage video object removal approach. Motivated by the observation that text condition is not best suited for this task, we simplify the pretrained video generation model by removing textual input and cross-attention layers, resulting in a more lightweight and efficient model architecture in the first stage. In the second stage, we distilled our remover on successful videos produced by the stage-1 model and curated by human annotators, using a minimax optimization strategy to further improve editing quality and inference speed. Specifically, the inner maximization identifies adversarial input noise ("bad noise") that makes failure removals, while the outer minimization step trains the model to generate high-quality removal results even under such challenging conditions. As a result, our method achieves a state-of-the-art video object removal results with as few as 6 sampling steps and doesn't rely on CFG, significantly improving inference efficiency. Extensive experiments demonstrate the effectiveness and superiority of MiniMax-Remover compared to existing methods. Codes and Videos are available at: https://minimax-remover.github.io.

Figures

Figures reproduced from arXiv: 2505.24873 by the authors.

Figure 1
Figure 1. Visual Results of MiniMax-Remover. The left side displays the original videos, while the [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. The comparison between different blocks. (a) the original Wan2.1 DiT block; (b) DiT block [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. The pipeline of our two-stage method. 3.2.2 Contrastive Conditioning for Object Removal We employ the positive condition token c + to guide the remover network in learning object removal and encourage the model to generate target objects under the guidance of c −. Specifically, when applying classifier-free guidance, c + serves as the positive condition and c − as the negative condition, steering the model away from… view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: The visual results of our object remover. The video on the left depicts the original video, [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Training framework of the Stage-1. (a) denotes the positive condition process, and the [PITH_FULL_IMAGE:figures/full_fig_p015_5.png]
Figure 6
Figure 6. Figure 6: Visual results of the Stage-1 Object Remover under inversion-based noises. The top row [PITH_FULL_IMAGE:figures/full_fig_p017_6.png]
Figure 7
Figure 7. Figure 7: Visual results of the Stage-1 Object Remover under adversarial-based noises. The top row [PITH_FULL_IMAGE:figures/full_fig_p018_7.png]
Figure 8
Figure 8. Figure 8: Visual Results of the Minimax-Remover with Adversarial-Based Noise. Adversarial-based [PITH_FULL_IMAGE:figures/full_fig_p018_8.png]

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Forward citations

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Reviewed August 7, 2026 · model on record in the stance chip above.