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ViBe: A Text-to-Video Benchmark for Evaluating Hallucination in Large Multimodal Models

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read ViBe introduces a benchmark categorizing hallucinations in text-to-video generation.

desk verdict Useful dataset, but the sampling story is undocumented and the 'detection' claim doesn't match the data; worth reviewing with major revision. read the letter →

arxiv 2411.10867 v2 pith:DLOXI7VQ submitted 2024-11-16 cs.CV cs.AI

Vipula Rawte , Sarthak Jain , Aarush Sinha , Garv Kaushik , Aman Bansal , Prathiksha Rumale Vishwanath , Samyak Rajesh Jain , Aishwarya Naresh Reganti
show 4 more authors
Vinija Jain Aman Chadha Amit P. Sheth Amitava Das
This is my paper · ORCID
classification cs.CVcs.AI
keywords text-to-videohallucinationbenchmarkvideoclassificationTimeSFormerMAEMSCOCOhumanannotation
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 ViBe, a human-annotated dataset of 3,782 videos generated by ten open-source text-to-video models from 837 MS COCO captions, with each video labeled as one of five hallucination types: Vanishing Subject, Omission Error, Numeric Variability, Subject Dysmorphia, and Visual Incongruity. The authors argue this is the first large-scale benchmark for systematically categorizing hallucinations in generated video, offering a more substantial resource than the prior 50-video benchmark. They also run classification baselines using VideoMAE and TimeSFormer embeddings with seven classifiers; the best configuration, TimeSFormer with a CNN, reaches 0.345 accuracy and 0.342 F1 score. The modest performance is presented as evidence that automated hallucination detection is difficult and that the dataset should drive the development of better methods.

What carries the argument

The central organizing device is the five-category taxonomy of text-to-video hallucinations: Vanishing Subject, Omission Error, Numeric Variability, Subject Dysmorphia, and Visual Incongruity. This taxonomy drives the manual annotation protocol that labels all 3,782 videos and defines the classification task. The benchmark pipeline then extracts video embeddings with pre-trained models, VideoMAE and TimeSFormer, and feeds them into classifiers; the best-performing configuration is TimeSFormer embeddings combined with a convolutional neural network.

What would settle it

Sampling the same ten T2V models with the same prompts and counting how many generated videos contain none of the five hallucination types would directly test the claim that these categories are the dominant failure modes; if a large share of videos fall outside the taxonomy, the benchmark's coverage claim collapses.

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Extended reading notes

Core claim

On its own terms, the paper claims to provide the first dataset specifically designed to systematically categorize hallucinations in text-to-video outputs. ViBe is constructed by prompting ten open-source T2V models with diverse MS COCO captions, manually annotating every generated video, and organizing the results into a five-category taxonomy that captures recurring failure patterns: subjects that vanish mid-video, objects omitted from the prompt, altered counts of objects, distorted forms, and physically impossible scenes. The paper further establishes a classification benchmark using video embeddings and reports baseline results, positioning ViBe as a foundation for evaluating prompt adherence and developing hallucination detectors for T2V models.

Load-bearing premise

The dataset is built only from videos that human annotators judged to contain hallucinations, and the paper does not report how many generated videos were excluded, so the benchmark's representativeness of text-to-video hallucination phenomena is unverified.

Editorial extensions

If this is right

  • Any text-to-video model can be evaluated against ViBe's five categories to identify its recurring failure patterns.
  • The five categories provide a shared vocabulary for comparing hallucination types across different T2V models.
  • The TimeSFormer + CNN baseline of 0.345 accuracy and 0.342 F1 gives a concrete reference point that future hallucination classifiers must exceed.
  • If the taxonomy holds, training detectors to flag specific hallucination types becomes a well-defined task, potentially enabling automatic evaluation and mitigation.
  • The dataset's scale, over 3,700 videos, supports training and testing that the previous 50-video benchmark could not.

Reading between the lines

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

  • Since ViBe contains only videos that human annotators judged to be hallucinated and the paper does not report how many generated videos were excluded, the dataset likely overstates how frequently hallucinations occur; a control set of non-hallucinated videos would be needed to build a true detector.
  • The single-label annotation protocol may compress cases where multiple hallucination types co-occur, so a multi-label variant could change baseline performance and better reflect real T2V errors.
  • With most videos lasting just one second, the distribution of hallucination types may not transfer to longer generations, where temporal consistency failures could appear differently.
  • A natural extension would be to use the five categories as fine-grained feedback signals for training T2V models with human preference alignment, rather than only for detection.
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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 / 5 minor

Summary. The paper introduces ViBe, a benchmark dataset of 3,782 videos generated by ten open-source text-to-video (T2V) models from 837 MS COCO captions. All videos were manually annotated into one of five hallucination categories: Vanishing Subject, Omission Error, Numeric Variability, Subject Dysmorphia, and Visual Incongruity. The authors report inter-annotator Cohen's kappa scores per category, extract video embeddings with TimeSFormer and VideoMAE, and evaluate seven classifiers on the resulting five-way classification task. The best baseline, TimeSFormer+CNN, reaches 0.345 accuracy and 0.342 F1. The paper claims to provide the first large-scale resource for systematically categorizing T2V hallucinations and a benchmark for hallucination detection.

Significance. If the dataset construction is sound, ViBe is a potentially valuable resource: it is substantially larger than the only comparable prior benchmark, T2VHaluBench (50 videos), and the five-category taxonomy is intuitive and illustrated with concrete examples. The reported kappa scores (0.7474--0.8737) indicate reasonable annotation consistency, and the classification baselines provide a useful reference point for future work. However, the current manuscript does not document the sampling/filtering protocol, does not include any non-hallucinated videos, and contains internal inconsistencies in reported counts; these issues must be resolved before the benchmark's representativeness and its 'hallucination detection' framing can be accepted.

major comments (3)
  1. [Section 3.4, Table 2] The dataset selection protocol is not disclosed. The paper never reports how many videos were generated per model, how many were excluded because they were judged non-hallucinated, or whether a per-category quota or cap was applied. The exact repeated counts of 70 in Table 2 (e.g., AnimateLCM 2/70/70/70/70, MagicTime 70/70/70/69/70, HotShotXL 70/70/70/69/70) strongly suggest an undocumented sampling cap. This is load-bearing because without the denominator and exclusion rule, Table 2 cannot be interpreted as a natural distribution of hallucinations across models, and no model-level hallucination rate can be computed. The authors should report the total number of generated videos per model, the inclusion/exclusion criteria, and any quota used.
  2. [Abstract, Section 4, Table 6] The benchmark contains only hallucinated videos: all 3,782 instances are positive examples, and the classification task has exactly five hallucination-type labels with no 'no hallucination' class. Consequently, the reported TimeSFormer+CNN result of 0.345 accuracy is five-way categorization accuracy conditional on a video already being known to be hallucinated; it is not hallucination detection. The abstract and Section 4 claim 'hallucination detection,' but the experimental setup cannot support such a claim. Either non-hallucinated videos should be included and a detection task defined, or the claims and title framing should be revised to 'hallucination-type classification.'
  3. [Section 3.5, Figure 6] The numerical values in Figure 6 are inconsistent with Table 2 and with the prose in the same paragraph. For example, Table 2 reports 282 AnimateLCM videos and 694 MORA videos, all 1 second long, so the cumulative durations should be 282 seconds and 694 seconds, respectively, but Figure 6 lists 349 seconds and 349 seconds. Similarly, zeroscope_v2_XL (363 videos at 2 seconds = 726 seconds) is labeled 311 seconds in the figure, while the text correctly says 726 seconds. This suggests the bar labels are misassigned. The figure must be corrected, and the corresponding values in Table 2 should be cross-checked, because the internal inconsistency weakens confidence in the reported dataset counts.
minor comments (5)
  1. [Section 3.5] The sentence beginning 'The total duration per model refers to the cumulative time spent annotating all videos...' is garbled ("as shown in 6. 1 provides a detailed report"); it should be rewritten for clarity.
  2. [Appendix B] The appendix states that the average annotation time was 2.25 seconds per video, which is implausible and contradicts Figure 7, where annotation times are hundreds or thousands of minutes per model. This is likely 2.25 minutes; please correct the units.
  3. [Section 2] The paragraph on image-generation hallucination benchmarks cites HADES (Liu et al., 2022) as an image-generation hallucination dataset, but the reference is actually a token-level text hallucination detection benchmark; the citation or the surrounding description should be corrected.
  4. [References] References to Chu et al. appear as both 2024a and 2024b for what appears to be the same Sora Detector paper; please deduplicate or clarify the distinction.
  5. [Section 3.6] The kappa values are reported per category, but the number of annotators who labeled the 100 common videos and the procedure for adjudicating disagreements are not described; a brief note on the annotation adjudication protocol would help.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: ViBe's hallucination labels are human annotations used to train and test classifiers on a held-out partition, so the benchmark results are not equivalent to the paper's inputs by construction.

full rationale

ViBe is a dataset-building and benchmark paper rather than a derivation. The five-category taxonomy is defined from observed video inconsistencies (Section 3.3), and the labels come from human annotators (Section 3.4: 'Five annotators manually categorized 3,782 videos, assigning each to the most prominent hallucination type based on a predefined taxonomy'). The classification experiments are a standard supervised evaluation: Section 4.2 states 'The dataset was partitioned into 80% for training and 20% for testing,' and Table 6 reports held-out accuracy and F1 for models trained on VideoMAE and TimeSFormer embeddings. No fitted parameter is renamed as a prediction, no result is justified solely by a self-citation, and no claimed derivation reduces to its own definition. The undocumented selection of hallucinated videos and the lack of a non-hallucinated class (Table 2, Table 6) are legitimate concerns about construct validity and representativeness, but they are data-provenance limitations, not circular reasoning. Accordingly, no circular step is identified and the score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper is an empirical benchmark construction; it introduces no fitted constants or invented physical entities. The five-category taxonomy and the single-label annotation convention are domain assumptions that shape the dataset, and the unstated selection procedure for hallucinated videos is a key assumption.

assumptions (3)
  • domain assumption The five-category taxonomy (VS, OE, NV, SD, VI) is exhaustive for T2V hallucinations.
    Section 3.3 defines categories based on observed inconsistencies; no coverage analysis or mention of categories not represented is provided.
  • domain assumption Human annotations of a single dominant category are reliable enough to serve as gold labels.
    Section 3.6 reports Cohen's Kappa on 100 videos only; agreement is moderate to high, but the full annotation set uses five annotators with no per-annotator reliability report.
  • domain assumption MS COCO captions are a representative prompt distribution for T2V evaluation.
    Section 3.1 selects 837 captions without reporting the sampling procedure, seed, or balance checks across the five thematic categories.

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Cite this review

Pith. "Pith review of ViBe: A Text-to-Video Benchmark for Evaluating Hallucination in Large Multimodal Models." pith.science (2026). https://pith.science/paper/DLOXI7VQ

@misc{pith2026241110867,
  author       = {Pith},
  title        = {Pith review of: ViBe: A Text-to-Video Benchmark for Evaluating Hallucination in Large Multimodal Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DLOXI7VQ}},
  note         = {Machine review of arXiv:2411.10867}
}
read the original abstract

Recent advances in Large Multimodal Models (LMMs) have expanded their capabilities to video understanding, with Text-to-Video (T2V) models excelling in generating videos from textual prompts. However, they still frequently produce hallucinated content, revealing AI-generated inconsistencies. We introduce ViBe (https://vibe-t2v-bench.github.io/): a large-scale dataset of hallucinated videos from open-source T2V models. We identify five major hallucination types: Vanishing Subject, Omission Error, Numeric Variability, Subject Dysmorphia, and Visual Incongruity. Using ten T2V models, we generated and manually annotated 3,782 videos from 837 diverse MS COCO captions. Our proposed benchmark includes a dataset of hallucinated videos and a classification framework using video embeddings. ViBe serves as a critical resource for evaluating T2V reliability and advancing hallucination detection. We establish classification as a baseline, with the TimeSFormer + CNN ensemble achieving the best performance (0.345 accuracy, 0.342 F1 score). While initial baselines proposed achieve modest accuracy, this highlights the difficulty of automated hallucination detection and the need for improved methods. Our research aims to drive the development of more robust T2V models and evaluate their outputs based on user preferences.

Figures

Figures reproduced from arXiv: 2411.10867 by the authors.

Figure 1
Figure 1. To generate the videos, we utilized randomly sampled image captions from the MS COCO dataset as [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Prompt: three guys are standing on a beach next to surfboards. Vanishing Subject: The prompt mentions that there are three guys on a beach with surfboards. In the initial frame, we see 3 guys on the beach with surfboards, but in the last frame, we find only two guys remaining. The third guy seems to have vanished. T2V Hallucination Categories Missing Elements Incorrect Elements Vanishing Subject Omission Error Numer… view at source ↗
Figure 3
Figure 3. Hierarchy of hallucination categories in [PITH_FULL_IMAGE:figures/full_fig_p002_3.png] view at source ↗
Figures from the paper (17 more)
Figure 4
Figure 4. Figure 4: Prompt: Two road workers are standing by a red light with a sign. Numeric Variability: The prompt explicitly mentions two road workers. However, while the system accurately incorporates elements like the red light and depicts one road worker standing, it fails to gener…
Figure 5
Figure 5. Figure 5: Prompt: A train heading for a curve in the track. Visual Incongruity: The scenario presents multiple logical and physical impossibilities in its temporal sequence. Initially, no train is visible in the first two frames, violating conservation of mass and the principle …
Figure 6
Figure 6. Figure 6: The total duration per model represents the [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 8
Figure 8. Figure 8: Prompt: A man in athletic wear swings a tennis racket through the air. Subject Dysmorphia: Throughout the video, both the man and the racket undergo visually inconsistent distortions, resulting in temporal and spatial anomalies. The system-generated artifacts introduce…
Figure 9
Figure 9. Figure 9: Prompt: a baby elephant walking behind a large one Omission Error: The generated output fails to render a critical component explicitly specified in the input prompt the larger one. While the baby elephant is depicted, the absence of the larger elephant represents a si…
Figure 10
Figure 10. Figure 10: This figure illustrates the annotation tool employed to label various video samples. The tool comprises [PITH_FULL_IMAGE:figures/full_fig_p012_10.png]
Figure 11
Figure 11. Figure 11: Using this annotation tool, annotators can classify the generated videos into one of the five predefined [PITH_FULL_IMAGE:figures/full_fig_p012_11.png]
Figure 12
Figure 12. Figure 12: Prompt: A boy in a red hat playing with tee ball set. Vanishing Subject: The visual content depicts a boy wearing a red hat engaged in play with a tee-ball set. However, a hallucination occurs within the generated scene, where the tee-ball set, initially present, inex…
Figure 13
Figure 13. Figure 13: Prompt: Two young boys playing Wii bowling on a large television screen Vanishing Subject: In the video frames, the TV initially displays two boys. However, as the video progresses, subtle changes occur. By the final frame, one of the boys on the TV has mysteriously v…
Figure 14
Figure 14. Figure 14: Prompt: A person on a skateboard with his arms in the air. Omission Error: The prompt describes a scene featuring a person on a skateboard with their arms raised in the air. However, this description exhibits a hallucination, as the video does not depict the individua…
Figure 15
Figure 15. Figure 15: Prompt: Blue and yellow flowers in a glass vase near a mirror. Omission Error: The video lacks any blue flowers, despite their explicit mention in the prompt. This discrepancy highlights a failure of the model to accurately represent key visual elements specified in t…
Figure 16
Figure 16. Figure 16: Prompt: A happy adult holding two large donuts. Numeric Variability: The description depicts a content scenario where a happy adult is holding two large donuts. However, a hallucination occurs within the video, where the depicted woman is shown holding three donuts in…
Figure 17
Figure 17. Figure 17: Prompt: A banana and a yellow apple in a woven basket. Numeric Variability: The visual scene consists of a woven basket containing one banana and one yellow apple. However, the generative output exhibits a hallucination, inaccurately depicting two bananas and two appl…
Figure 18
Figure 18. Figure 18: Prompt: Skateboarder and blue shirt and black jeans jumping on his board Subject Dysmorphia: The video depicts a person riding a skateboard. Throughout the frames, the wheels of the skateboard keep morphing, fluctuating in number as they increase and decrease. Additio…
Figure 19
Figure 19. Figure 19: Prompt: A woman is jumping on a white bed. Subject Dysmorphia: The video depicts a woman jumping on a white bed. Over time, a hallucination effect manifests, leading to a dysmorphic transformation of the woman’s face within the video [PITH_FULL_IMAGE:figures/full_fig…
Figure 20
Figure 20. Figure 20: Prompt: A crowd of people standing on a beach flying kites. Visual Incongruity: Instead of being depicted in the sky as expected, the kites appear visually inconsistent, resembling objects embedded in the sand [PITH_FULL_IMAGE:figures/full_fig_p014_20.png]
Figure 21
Figure 21. Figure 21: Prompt: a animal that is walking in a crowd of people Visual Incongruity: In the generated video, a stone statue of an animal is seen moving atop a vast crowd that appears to be composed of human heads. The statue’s movement contrasts with its rigid, lifeless material…

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Forward citations

Cited by 2 Pith papers

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    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.