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REVIEW 4 major objections 7 minor 39 references

BrokenVideos: A Benchmark Dataset for Fine-Grained Artifact Localization in AI-Generated Videos

T0 review · 4 major / 7 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read BrokenVideos is a new benchmark of 3,254 AI-generated videos with pixel-level artifact masks, and fine-tuning three current video-segmentation models on it sharply improves their localization of corrupted regions.

desk verdict A genuinely useful dataset idea whose central claim — mask quality — is currently asserted rather than demonstrated; worth refereeing if the authors release artifacts and report annotation reliability. read the letter →

arxiv 2506.20103 v1 pith:6L6PS34C submitted 2025-06-25 cs.CV cs.AI

Jiahao Lin , Weixuan Peng , Bojia Zi , Yifeng Gao , Xianbiao Qi , Xingjun Ma , Yu-Gang Jiang This is my paper
classification cs.CVcs.AI
keywords AI-generatedvideoartifactlocalizationfine-grainedsegmentationmasksbenchmarkdatasettext-to-videoartifactsMLLMfine-tuningqualityassessmentSAM2annotation
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 introduces BrokenVideos, a benchmark of 3,254 AI-generated videos in which every video carries pixel-level masks marking regions of visual corruption. Its central claim is that these masks form a usable ground-truth signal: fine-tuning current multimodal large language model (MLLM) based video-segmentation models on the dataset sharply improves their ability to localize artifacts in synthetic video, with gains reported on both artifact-heavy and normal videos. If true, the dataset fills a missing resource for artifact localization, with direct value for automated quality control and for diagnosing weaknesses in generative video models. The paper also proposes a five-category artifact taxonomy to make annotation reproducible.

What carries the argument

The load-bearing object is the annotation pipeline: a SAM2-based interactive GUI (SAM2-GUI) that lets annotators click positive and negative prompts to create multi-instance masks per frame, then propagates those masks through the video with a video-tracking segmenter for frame-by-frame refinement. This pipeline produced pixel-level ground truth for 3,254 videos and roughly 336,000 artifact region annotations. On the modeling side, the fine-tuning recipe pairs artifact videos with a fixed 'uncanny artifact' prompt and normal videos with a no-artifact prompt, then fine-tunes three text-guided video-segmentation MLLMs; the masks supply the supervision.

What would settle it

Take a random subset of BrokenVideos videos, have independent annotators re-mask them with the same protocol, and compute region-similarity $J$ between the two mask sets; low overlap would show the ground truth is not stable, and a fine-tuned model that performs no better than an identical model trained on shuffled masks would show the masks are not driving the improvement.

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Extended reading notes

Core claim

The discovery is that fine-grained artifact localization in AI-generated video can be treated as a supervised video-segmentation problem, and existing models that are near-random at this task before fine-tuning become effective after training on the dataset. On broken videos, GLUS's $J\&F$ score rises from 0.1202 to 0.6387, SAMWISE's from 0.0513 to 0.5403, and VideoLISA's from 0.2383 to 0.6301; gains also appear on normal videos, suggesting the models learn to suppress false positives. The dataset contains roughly 336,000 manual region annotations spanning technical, semantic, cognitive, consistency, and instructional artifact categories.

Load-bearing premise

The load-bearing premise is that the pixel-level masks accurately capture the artifacts as humans perceive them; if the masks are noisy or inconsistent, the fine-tuning gains mainly reflect learning that annotation noise rather than real localization ability.

Editorial extensions

If this is right

  • Fine-tuning GLUS, SAMWISE, or VideoLISA on BrokenVideos raises artifact-localization $J\&F$ scores on broken videos by roughly 38 to 52 points.
  • The same fine-tuning improves scores on normal videos, indicating a reduction in false-positive artifact predictions rather than indiscriminate flagging.
  • The dataset's multi-instance masks allow evaluation of videos where several artifacts overlap spatially and temporally.
  • Because masks are exported as frame-level PNGs, they can be plugged into standard video-segmentation pipelines with minimal adaptation.
  • BrokenVideos provides a common training and evaluation set for the emerging task of artifact localization in generative video.

Reading between the lines

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

  • If the per-mask category labels are released, the same ground truth could train artifact-type classifiers, not just region localizers.
  • The improvement on normal videos, taken alone, does not prove the models learned artifact semantics; a control fine-tune on randomly placed masks would separate true signal from domain adaptation.
  • Because part of the dataset reuses source videos from an earlier benchmark, users should check for overlap with that benchmark's test splits before treating BrokenVideos as fully independent.
  • An inter-annotator agreement study on a random mask sample would turn the human-inspection claim into a measured quantity and let users calibrate expected mask noise.
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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 introduces BrokenVideos, a dataset of 3,254 AI-generated videos with pixel-level artifact masks, constructed using a SAM2-based interactive annotation tool and human inspection, and organized under a five-category taxonomy of artifacts. The authors fine-tune three video localization models (GLUS, SAMWISE, VideoLISA) on a training subset of the dataset and report large improvements on a held-out subset, e.g., GLUS J&F on broken videos rising from 0.1202 to 0.6387, and interpret these gains as evidence that the dataset is useful for training and evaluating artifact localization models.

Significance. If the mask quality is trustworthy, BrokenVideos would fill a clear gap: current benchmarks for AI-generated video focus on binary real/fake classification or global quality, not fine-grained spatial artifact localization. The paper's strengths include a relatively large collection, a structured taxonomy, a semi-automatic annotation pipeline, and fine-tuning protocols for three modern MLLM-based segmentation models. The quantitative improvements in Table 2 are large and consistent across models, suggesting that the dataset can provide a useful learning signal. However, the significance is conditional on evidence that the pixel masks reflect human agreement about artifact locations, that the dataset statistics are internally consistent, and that the learned localizers generalize beyond the same-distribution holdout. The paper currently provides no inter-annotator agreement, no adjudication statistics, no external validation, and no reconciliation of the video counts, so the central claim of a high-quality benchmark remains unverified.

major comments (4)
  1. [Section 3.3]
  2. [Section 3.2.1 / Table 1]
  3. [Section 4.1]
  4. [Section 4.3 / Section 5]
minor comments (7)
  1. [Abstract]
  2. [Abstract / Section 1]
  3. [Section 4.1]
  4. [Table 1]
  5. [Figures 1 and 4]
  6. [Section 4.2]
  7. [Section 3.2]

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the fine-tuning gains are measured on a held-out split of an independently annotated benchmark, not reduced to the training inputs by construction.

full rationale

BrokenVideos is a dataset contribution whose central claim is empirical: fine-tuning video segmentation models and MLLMs on the dataset improves artifact localization (Section 4, Table 2). The evaluation uses a held-out validation set of 300 videos (150 broken, 150 normal), while training uses a separate set of 3,091 broken and 748 clean videos (Section 4.1). This is standard supervised training/evaluation on a self-defined benchmark, not a derivation that reduces to its own inputs. The ground-truth masks are produced by a SAM2-GUI annotation tool with human inspection and refinement (Section 3.2.1); no fitted parameter is relabeled as a prediction, no uniqueness theorem or self-citation chain is invoked, and the artifact taxonomy is an annotation schema rather than a mathematical construction that presupposes the results. Concerns about mask reliability (no inter-annotator agreement, non-localizable artifact categories such as omissions) and the apparent arithmetic inconsistency between the total 3,254 videos and the training/validation counts are validity and quality issues, not circularity. Therefore no circular step is exhibited.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No free parameters or invented physical entities appear. The central claim rests entirely on annotation validity, dataset provenance, and evaluation assumptions listed above.

assumptions (4)
  • domain assumption The five-category artifact taxonomy is sufficient and consistently applicable to artifacts in AI-generated videos.
    Invoked in Section 3.2.1 and Table 1. If annotators do not share this taxonomy, the mask labels are inconsistent and the benchmark ground truth is ill-defined.
  • domain assumption Human annotators using SAM2-GUI produced accurate, consistent pixel-level masks, and temporal propagation did not introduce significant errors.
    Stated in Sections 3.2.1 and 3.3 as 'careful human inspection', but no inter-annotator agreement, adjudication, or error analysis is reported. All downstream fine-tuning results inherit this premise.
  • domain assumption The videos reused from benchmark [33] are representative of current AI-generated video artifacts and were selected without bias.
    Section 3.2 says the dataset 'leverages the existing benchmarking dataset [33]' without giving selection criteria, source models, or proportions, so diversity and coverage claims depend on it.
  • domain assumption Fine-tuning on BrokenVideos and evaluating on a held-out split of the same dataset measures artifact-localization ability rather than dataset-specific overfitting.
    Section 4.3 evaluates only on a self-split test set. The claim that training on BrokenVideos 'significantly improves' localization requires this premise because no external generator or benchmark is tested.

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Cite this review

Pith. "Pith review of BrokenVideos: A Benchmark Dataset for Fine-Grained Artifact Localization in AI-Generated Videos." pith.science (2026). https://pith.science/paper/6L6PS34C

@misc{pith2026250620103,
  author       = {Pith},
  title        = {Pith review of: BrokenVideos: A Benchmark Dataset for Fine-Grained Artifact Localization in AI-Generated Videos},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6L6PS34C}},
  note         = {Machine review of arXiv:2506.20103}
}
read the original abstract

Recent advances in deep generative models have led to significant progress in video generation, yet the fidelity of AI-generated videos remains limited. Synthesized content often exhibits visual artifacts such as temporally inconsistent motion, physically implausible trajectories, unnatural object deformations, and local blurring that undermine realism and user trust. Accurate detection and spatial localization of these artifacts are crucial for both automated quality control and for guiding the development of improved generative models. However, the research community currently lacks a comprehensive benchmark specifically designed for artifact localization in AI generated videos. Existing datasets either restrict themselves to video or frame level detection or lack the fine-grained spatial annotations necessary for evaluating localization methods. To address this gap, we introduce BrokenVideos, a benchmark dataset of 3,254 AI-generated videos with meticulously annotated, pixel-level masks highlighting regions of visual corruption. Each annotation is validated through detailed human inspection to ensure high quality ground truth. Our experiments show that training state of the art artifact detection models and multi modal large language models (MLLMs) on BrokenVideos significantly improves their ability to localize corrupted regions. Through extensive evaluation, we demonstrate that BrokenVideos establishes a critical foundation for benchmarking and advancing research on artifact localization in generative video models. The dataset is available at: https://broken-video-detection-datetsets.github.io/Broken-Video-Detection-Datasets.github.io/.

Figures

Figures reproduced from arXiv: 2506.20103 by the authors.

Figure 1
Figure 1. Visualization of BrokenVideos. Best viewed using Acrobat Reader. Click on the images to play the animation clips. Despite these advances, the fidelity of AI-generated videos re￾mains far from perfect. Even the most advanced models frequently produce visual artifacts, such as temporally inconsistent motion, unnatural object deformations, and local blurring—that disrupt the illusion of realism and diminish user trust.… view at source ↗
Figure 2
Figure 2. Visualization of our artifact annotation strategy. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Topic distribution of videos in BrokenVideos. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Comparison of various baseline artifact localization models and models trained on our BrokenVideos dataset. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

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