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REVIEW 5 major objections 6 minor 3 cited by

AIGV-Assessor: Benchmarking and Evaluating the Perceptual Quality of Text-to-Video Generation with LMM

T0 review · 5 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read AIGV-Assessor claims to outperform all existing methods for predicting both MOS and pairwise preferences on AI-generated video quality, across four dimensions.

desk verdict A large, genuinely useful AIGVQA benchmark with a credible but unremarkable assessor; the pairwise-label noise and missing statistics make the pair-ranking SOTA claim the weak point. read the letter →

arxiv 2411.17221 v1 pith:4ONQT7H6 submitted 2024-11-26 cs.CV

Jiarui Wang , Huiyu Duan , Guangtao Zhai , Juntong Wang , Xiongkuo Min This is my paper · ORCID
classification cs.CV
keywords text-to-videogenerationvideoqualityassessmentlargemultimodalmodelspairwisepreferencemeanopinionscorebenchmarkdatasetspatiotemporalfeatures
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 tries to establish that AI-generated video quality can be measured automatically in a way that matches human preference across four distinct axes: static quality, temporal smoothness, dynamic degree, and text-video correspondence. To do this, it builds AIGVQA-DB, a dataset of 36,576 videos from 15 text-to-video generators and 1,048 prompts, annotated with both mean opinion scores and 30,000 pairwise comparisons. On top of the dataset, it proposes AIGV-Assessor, an LMM-based model that reads spatiotemporal visual features along with the prompt, outputs a quality-level description and a numerical score, and is fine-tuned to decide which of two videos is better. The paper reports that AIGV-Assessor outperforms existing scoring and evaluation methods on all four dimensions and on several other AIGV quality benchmarks. If correct, this gives the field a more human-aligned automatic evaluator for text-to-video models.

What carries the argument

AIGV-Assessor combines a 2D vision encoder (InternViT) for per-frame spatial content and a 3D SlowFast network for motion, projects both into the language space of an LLM (InternLM2-Chat-8B), and uses the LLM's hidden states for quality regression. The model is trained in three stages: aligning visual tokens with language, fine-tuning with LoRA and an L1 loss against MOS, and adding a pairwise comparison stage that uses an LPIPS-inspired judge network with cross-entropy loss. This design lets the same model produce quality-level text, numerical scores, and pairwise preferences.

What would settle it

Re-annotate a random subset of about 1,000 pairs with at least ten annotators each; if AIGV-Assessor's pair accuracy on this higher-confidence ground truth is substantially below the level it achieves on the original three-annotator majority labels, the claimed alignment with human preference would be overstated.

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Extended reading notes

Core claim

The central claim is that AIGV-Assessor achieves state-of-the-art performance for both MOS prediction and pair ranking on AIGVQA-DB, beating prior handcrafted, deep-learning, vision-language, and LMM-based methods on all four evaluated dimensions. The paper further claims that the model generalizes: it posts the best correlations on the LGVQ, FETV, T2VQA-DB, and GAIA benchmarks, and its predicted model rankings overlap most closely with ground-truth rankings among the compared evaluators.

Load-bearing premise

The pairwise labels are trustworthy enough to serve as both training signal and evaluation ground truth, but each pair is judged by only three annotators with no reported agreement or noise analysis.

Editorial extensions

If this is right

  • Text-to-video models can now be ranked automatically on four separate quality axes, not just a single aggregate score.
  • The pairwise comparison subset offers a training signal that is complementary to MOS, reducing ambiguity of absolute ratings on high-quality content.
  • The dataset's prompt categorization (spatial content, temporal content, attribute control, complexity) allows diagnosing which video generators fail on which prompt types.
  • Existing AIGVQA benchmarks without pairwise data can still be evaluated by the model, giving cross-dataset comparisons.
  • The reported gains on external benchmarks suggest the method transfers beyond its own training distribution.

Reading between the lines

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

  • If the pair labels are reliable, the three-annotator majority-vote design could be adopted by future dataset builders as a cheaper alternative to full MOS annotation for preference modeling.
  • The same spatiotemporal-plus-LMM architecture could be applied to other generative media, such as image-to-video or audio-driven video, where distinct quality axes also matter.
  • The prompt categorization could be used to build targeted stress tests for text-to-video models, testing specific failure modes like event-order violations or camera-view control.
  • The model's reliance on paired fine-tuning suggests that adding more pairwise data, or using synthetic pairs from strong generators, may further improve alignment with human preference.
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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

5 major / 6 minor

Summary. The paper introduces AIGVQA-DB, a large-scale text-to-video quality assessment dataset of 36,576 AI-generated videos from 15 generation models and 1,048 prompts, annotated with MOS and pairwise comparisons across four perceptual dimensions (static quality, temporal smoothness, dynamic degree, text-video correspondence). It also proposes AIGV-Assessor, an LMM-based model that fuses spatial (InternViT) and temporal (SlowFast) features, produces quality-level descriptions and regression scores, and is fine-tuned for pairwise preference prediction. The authors report state-of-the-art performance on AIGVQA-DB and on four external AIGVQA datasets (LGVQ, FETV, T2VQA-DB, GAIA).

Significance. If the results are reliable, the dataset is a substantial community resource: 36,576 videos, 370k expert ratings, 30,000 annotated pairs, and a four-dimension annotation protocol go well beyond existing AIGVQA benchmarks in scale and scope. The proposed architecture is plausible, and the evaluation across five datasets with many baselines is commendable. The authors also release the dataset and code, which supports reproducibility. The central claim, however, rests on the quality of the pairwise labels and on the statistical strength of the reported gains; both need strengthening before the state-of-the-art claim can be accepted.

major comments (5)
  1. [Section 3.3 and Appendix C.3] The pairwise ground truth is produced by three annotators per pair with majority voting, but no inter-rater agreement statistic, per-annotator accuracy, or label-noise analysis is reported. The description in Appendix C.3 that ties are resolved by 'the average opinions of the three participants' is undefined for an odd number of binary votes. Since the same pairwise labels are used both as the stage-3 training target (Appendix E, Eq. 3) and as the evaluation ground truth for the Pair Acc column of Table 3, the reported pair accuracy is accuracy against this noisy proxy. Please report inter-rater agreement, the distribution of majority margins (2-1 vs 3-0), a label-noise analysis, and, if possible, an external pairwise dataset for validation.
  2. [Section 5.1 and Tables 3-6] All results are averages over ten random splits, yet no standard deviations, confidence intervals, or significance tests are provided. Several headline gains over strong baselines are small: on LGVQ temporal smoothness AIGV-Assessor improves SRCC from 0.893 to 0.900 and PLCC from 0.907 to 0.920; on T2VQA-DB SRCC improves from 0.7965 to 0.8131. Because the differences are on the order of 0.007-0.02, significance testing is required to support the claim of 'state-of-the-art performance' over the best existing methods.
  3. [Section 3.1, Appendix D.1, and Table 9] The dataset description is internally inconsistent. The abstract and introduction state 15 text-to-video models, but Section 3.1 says the MOS subset uses 12 generative models and the pair-comparison subset uses 12 generative models. Table 9 lists 15 models, with Gen-2, MoonValley, and Sora appearing only in the MOS subset and MorphStudio only in the pair subset; it is unclear how the 576 MOS videos and 36,000 pair videos combine into the stated total of 36,576, and whether the MOS subset overlaps with the pair subset. Please clarify the exact composition of each subset and the total number of distinct videos.
  4. [Section 4.2 and Appendix E] The pairwise comparison training stage uses a 'judge network inspired by LPIPS' whose architecture, parameterization, and training status (frozen or trainable) are not specified. This makes it impossible to determine how much of the Pair Acc gain comes from the judge network versus the quality regression head. Please provide the full architecture, input features, and training details of the judge network.
  5. [Table 3 evaluation protocol] For methods that do not have a dedicated pair-ranking head, it is unclear how Pair Acc is computed: whether each pair is classified by comparing the predicted scores of the two videos, or by feeding the pair into the model. This affects the fairness of the comparison, since AIGV-Assessor has an extra pairwise training stage. Please state the pair-evaluation protocol for all baselines and for AIGV-Assessor.
minor comments (6)
  1. [References] Reference [16] is cited for 'ITU-R BT.500-14' but the listed reference is a paper on confusing image quality assessment, not the ITU recommendation; please cite the correct standard.
  2. [References] Reference [68] is cited as InternViT and InternVL2-8B, but the reference is a paper about Charxiv chart understanding; this appears to be the wrong citation for the vision backbone and LLM used in the method.
  3. [Figure 5] The label 'Temporal smooothness' in Figure 5(a) contains a typo ('smooothness').
  4. [Table 3] Several entries in Table 3 have formatting errors, such as '55.08%0.4594 0.4701' in the BVQA row and '0.8489' appearing in the simpleVQA row in a position that is inconsistent with the other rows; please correct the table formatting and verify the reported values.
  5. [Appendix C.1] The heading 'Annotaion Criteria' contains a typo; it should read 'Annotation Criteria'.
  6. [Figure 17] The text-video correspondence criterion contains a double comma in the description of the 'Bad' level ('either missing or incorrectly represented, , resulting').

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the model is trained and evaluated on held-out splits of AIGVQA-DB and on external AIGVQA benchmarks, with no fitted quantity relabeled as a prediction.

full rationale

The paper's central claims are empirical rather than derivational. AIGV-Assessor is trained on AIGVQA-DB using human MOS and pairwise labels (Sections 3.2, 3.3, 4.2, Appendix E), then evaluated on held-out splits of AIGVQA-DB (Section 5.1: 'all experiments for each method are retrained on each dimension using the same training and testing split as the previous literature at a ratio of 4:1') and on four external datasets, LGVQ, FETV, T2VQA-DB, and GAIA (Tables 4-6). No equation defines an output quantity in terms of the model's own prediction, and no fitted constant is renamed as a prediction. The frequent references to prior benchmarks from overlapping research groups (e.g., GAIA [11], T2VQA-DB [30], LGVQ [84]) function as external test beds, not as premises that force the reported state-of-the-art result. The weakest link, that pairwise ground truth comes from three-annotator majority votes with no reported inter-rater agreement or label-noise analysis (Appendix C.3), is a correctness or robustness concern about label reliability, not a circularity: using the same noisy labels for training and held-out evaluation is standard supervised benchmarking and does not make the claimed improvement reduce to its inputs by construction. No circular step satisfying the quoted-evidence requirement was found, so the appropriate finding is no significant circularity.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new physical or conceptual entities; AIGVQA-DB and AIGV-Assessor are artifacts, not postulated entities. The central claims rest on the annotation protocol, the reliability of majority-vote pair labels, and the choice of spatiotemporal plus LLM architecture, all of which are domain assumptions rather than independently verified facts.

free parameters (2)
  • LoRA rank and alpha = not reported
    LoRA fine-tuning of the vision encoder and LLM is core to training stages 2 and 3. Without rank, alpha, and target modules, the model cannot be reproduced, and these choices materially affect performance.
  • Quality regression head weights = learned on AIGVQA-DB MOS labels
    The regression head maps LLM hidden states to four MOS values. This is the component that produces the headline scores and is trained directly on the dataset's MOS labels.
assumptions (4)
  • domain assumption Z-score rescaling of raw ratings produces an interval-scale MOS comparable across subjects.
    Section 3.3 uses z'_ij = 100(z_ij + 3)/6 to rescale raw scores to [0,100]. This assumes subjects use the rating scale similarly and that the standardized values can be averaged meaningfully, with no outlier removal or subject-reliability filtering described.
  • domain assumption Majority vote of three annotators is a sufficient ground-truth label for 30,000 sampled pairs.
    Section 3.3 and Appendix C.3 state each pair is judged by three annotators and the final label is the majority vote. No inter-annotator agreement statistics are provided, so the reliability of these labels as training and evaluation targets is assumed.
  • domain assumption The four annotation dimensions are separable and consistently understood by annotators.
    Section 3.2 defines static quality, temporal smoothness, dynamic degree, and text-video correspondence as distinct perspectives. Annotators rate each dimension separately, which requires these dimensions to be sufficiently independent and consistently interpreted despite likely correlations.
  • ad hoc to paper InternViT plus SlowFast features and InternLM2 represent the perceptual attributes relevant to human preference.
    Section 4.1 fixes the spatial and temporal encoders and the LLM without an ablation on encoder choice. The model's success depends on this inductive bias, which is introduced by the paper rather than independently established.

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Cite this review

Pith. "Pith review of AIGV-Assessor: Benchmarking and Evaluating the Perceptual Quality of Text-to-Video Generation with LMM." pith.science (2026). https://pith.science/paper/4ONQT7H6

@misc{pith2026241117221,
  author       = {Pith},
  title        = {Pith review of: AIGV-Assessor: Benchmarking and Evaluating the Perceptual Quality of Text-to-Video Generation with LMM},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4ONQT7H6}},
  note         = {Machine review of arXiv:2411.17221}
}
read the original abstract

The rapid advancement of large multimodal models (LMMs) has led to the rapid expansion of artificial intelligence generated videos (AIGVs), which highlights the pressing need for effective video quality assessment (VQA) models designed specifically for AIGVs. Current VQA models generally fall short in accurately assessing the perceptual quality of AIGVs due to the presence of unique distortions, such as unrealistic objects, unnatural movements, or inconsistent visual elements. To address this challenge, we first present AIGVQA-DB, a large-scale dataset comprising 36,576 AIGVs generated by 15 advanced text-to-video models using 1,048 diverse prompts. With these AIGVs, a systematic annotation pipeline including scoring and ranking processes is devised, which collects 370k expert ratings to date. Based on AIGVQA-DB, we further introduce AIGV-Assessor, a novel VQA model that leverages spatiotemporal features and LMM frameworks to capture the intricate quality attributes of AIGVs, thereby accurately predicting precise video quality scores and video pair preferences. Through comprehensive experiments on both AIGVQA-DB and existing AIGV databases, AIGV-Assessor demonstrates state-of-the-art performance, significantly surpassing existing scoring or evaluation methods in terms of multiple perceptual quality dimensions.

Figures

Figures reproduced from arXiv: 2411.17221 by the authors.

Figure 1
Figure 1. An overview of the AIGVQA-DB construction pipeline, illustrating the generation and the subjective evaluation procedures for [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Video score distribution from the four perspectives including static quality, temporal smoothness, dynamic degree, and t2v [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Comparison of averaged win rates of different generation models across different categories. (a) Results across prompt complex [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (14 more)
Figure 4
Figure 4. Figure 4: (a) Comparison of text-to-video generation models re [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: The framework of AIGV-Assessor: (a) AIGV-Assessor takes AI-generated video frames as input and outputs both text-based [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 6
Figure 6. Figure 6: Comparison of win rates of different generation models across four dimensions evaluated by different VQA methods, demon [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: The motivation for visual quality comparison: single [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 8
Figure 8. Figure 8: An example of the rating assessment interface for human evaluation. The subjects are instructed to rate four dimensions of [PITH_FULL_IMAGE:figures/full_fig_p017_8.png]
Figure 9
Figure 9. Figure 9: An example of the pair comparison assessment interface for human evaluation. The subjects are instructed to choose which [PITH_FULL_IMAGE:figures/full_fig_p017_9.png]
Figure 10
Figure 10. Figure 10: Comparison of averaged win rates of different generation models across different categories. (a) Results across spatial major [PITH_FULL_IMAGE:figures/full_fig_p019_10.png]
Figure 11
Figure 11. Figure 11: Descriptions and examples of the spatial major contents. [PITH_FULL_IMAGE:figures/full_fig_p023_11.png]
Figure 12
Figure 12. Figure 12: Descriptions and examples of the temporal major contents. [PITH_FULL_IMAGE:figures/full_fig_p024_12.png]
Figure 13
Figure 13. Figure 13: Descriptions and examples of the attribute control. [PITH_FULL_IMAGE:figures/full_fig_p024_13.png]
Figure 14
Figure 14. Figure 14: Instructions and examples for manual evaluation of [PITH_FULL_IMAGE:figures/full_fig_p025_14.png]
Figure 15
Figure 15. Figure 15: Instructions and examples for manual evaluation of [PITH_FULL_IMAGE:figures/full_fig_p026_15.png]
Figure 16
Figure 16. Figure 16: Instructions and examples for manual evaluation of [PITH_FULL_IMAGE:figures/full_fig_p027_16.png]
Figure 17
Figure 17. Figure 17: Instructions and examples for manual evaluation of [PITH_FULL_IMAGE:figures/full_fig_p028_17.png]

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Forward citations

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Pith tools

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