Content-Rich AIGC Video Quality Assessment via Intricate Text Alignment and Motion-Aware Consistency

Bowen Qu; Shangkun Sun; Wei Gao; Xiaoyu Liang

REVIEW 4 major objections 4 minor 1 cited by

Content-Rich AIGC Video Quality Assessment via Intricate Text Alignment and Motion-Aware Consistency

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

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Pith's one-line read CRAVE is a new quality evaluator that claims to align AI-generated video assessment with human opinion by fusing long text prompts with motion-aware video understanding, and it reports state-of-the-art correlations on two AIGC benchmarks.

desk verdict A useful new benchmark and a sensible method for AIGC VQA, but the CRAVE-DB evaluation protocol is underspecified enough that the headline SOTA margins on that benchmark are not yet trustworthy. read the letter →

arxiv 2502.04076 v1 pith:2N4R4TFM submitted 2025-02-06 cs.CV

Shangkun Sun , Xiaoyu Liang , Bowen Qu , Wei Gao This is my paper · ORCID

classification cs.CV

keywords AIGCvideoqualityassessmenttext-to-videoevaluationtext-videoalignmentmotionfidelitybenchmarkmulti-granularitytextfusionopticalflowhumanperception

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

This paper proposes CRAVE, an automatic quality-assessor for AI-generated videos from next-generation text-to-video models such as Sora, which produce long, motion-rich clips and accept elaborate prompts. CRAVE scores a video from three perspectives: traditional visual aesthetics and distortion, semantic alignment between the prompt and the video at paragraph, phrase, and word granularity, and motion fidelity judged from both optical flow and abstract action features. The paper also introduces CRAVE-DB, a benchmark of 1,228 videos generated by Kling, Qingying, Vidu, and Sora from 410 intricate prompts, each scored by 29 human annotators. The paper reports that CRAVE achieves the highest Spearman rank correlation with human scores on both CRAVE-DB and the existing T2VQA-DB, outperforming prior AIGC video quality methods. If those results hold, CRAVE and CRAVE-DB give the field a way to track and compare the perceptual quality of Sora-era video generators automatically.

What carries the argument

The load-bearing components are two named modules. MTT (multi-granularity text-temporal fusion) takes a frozen BLIP visual encoder with a temporal adapter to get video features, splits the prompt into words and phrases with SpaCy, adds a word-to-phrase mapping, and sums cosine distances between video features and text embeddings at paragraph, phrase, and word levels, so the alignment score uses several semantic granularities at once. HMM (hybrid motion-fidelity modeling) concatenates low-level dense motion features from the StreamFlow optical-flow model with high-level action features from Uniformer, capturing both frame-level irregularities and holistic motion plausibility. A third branch reuses DOVER's aesthetic and technical encoders for visual harmony, and the three outputs are regressed to a quality score under a rank-plus-PLCC loss. MTT carries the text-video alignment, HMM carries the temporal artifact scoring, and the DOVER branch carries aesthetics and distortion.

What would settle it

Train CRAVE on CRAVE-DB with several random 80/20, 70/30, and 60/40 train/test splits of the 1,228 videos, keeping the same 40-epoch schedule, and report SRCC/PLCC with standard deviations; if the mean SRCC falls below the T2VQA baseline of 0.7266 under any plausible split, the claimed CRAVE-DB lead is an artifact of the undisclosed split protocol.

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Extended reading notes

Core claim

On its own terms, the paper's central claim is that a three-branch evaluator — visual harmony, multi-granularity text-temporal alignment, and hybrid motion-fidelity modeling — predicts human Mean Opinion Scores for content-rich AIGC videos better than existing VQA methods. The reported evidence is a set of correlation numbers: on CRAVE-DB, CRAVE reaches SRCC 0.7587 and PLCC 0.7581, versus 0.7266 and 0.7098 for the strongest prior method T2VQA; on T2VQA-DB, CRAVE reaches SRCC 0.8122 and PLCC 0.8214, versus 0.7965 and 0.8066 for T2VQA. The authors interpret the gains as showing that long-prompt semantic alignment and motion-aware consistency are the missing ingredients in AIGC video quality assessment, and that the proposed CRAVE-DB closes the gap between existing datasets and the content richness of next-generation generators.

Load-bearing premise

The load-bearing premise is that the CRAVE-DB evaluation protocol is sound, yet the paper never states the train/test split ratio or how prompts and videos were assigned, so the reported state-of-the-art numbers on that benchmark rest on an unspecified design choice.

Editorial extensions

If this is right

On CRAVE-DB, CRAVE reports SRCC 0.7587, PLCC 0.7581, and KRCC 0.5660, above the best fine-tuned baseline T2VQA (0.7266, 0.7098, 0.5369), so the architecture transfers to next-generation AIGC videos.
On T2VQA-DB, CRAVE reports SRCC 0.8122, PLCC 0.8214, KRCC 0.6338, above T2VQA (0.7965, 0.8066, 0.6058), so the method also holds up on older, shorter-prompt videos.
The ablations show each designed component contributes: temporal-text fusion lifts SRCC on CRAVE-DB from 0.6512 to 0.7587, and combining granularity levels and hybrid motion modeling each add further gains.
CRAVE trained on either dataset can rank next-generation generators (Hunyuan, Sora, Seaweed Pro, Mochi 1 and others) zero-shot on VideoGenEval prompts, giving a tool for model comparison without new human ratings.
CRAVE-DB itself, with 410 elaborate prompts and 29 annotators per video, is proposed as a public benchmark for assessing future AIGC videos.

Reading between the lines

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

Editorial extension: the unspecified train/test split in Section 5.1 means the CRAVE-DB leaderboard numbers should be re-verified under a documented split before being used as a benchmark reference.
Editorial extension: the same multi-granularity text-temporal alignment could in principle serve as a reward model for fine-tuning or reranking video generators, since it produces a differentiable alignment score; the paper does not test this.
Editorial extension: because using 16 optical-flow frames outperformed 4 and 8, motion-quality assessment likely benefits from still longer temporal contexts; testing 32 or 64 frames would show whether the trend continues.
Editorial extension: CRAVE-DB's prompt-collection pipeline (dense captions summarized by GPT-4, then structured into shot/subject/action/scene/detail) could be reused to build benchmarks for other text-conditioned generative media, but the paper only applies it to video.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The real asset here is CRAVE-DB: 1,228 Sora-era videos, 410 long prompts averaging 68 words, 29 annotators, and clean MOS collection with z-scoring and BT.500 screening. That is a genuinely useful resource for the AIGC VQA subfield, and the prompt-construction pipeline (ShareGPT-4o summaries plus manual curation) is thoughtful. The method, CRAVE, is a fair combination of existing pieces—DOVER, BLIP with temporal adapters, StreamFlow optical flow, Uniformer—plus multi-granularity text fusion. That fusion is a reasonable extension of TriVQA and T2VQA, not a breakthrough, but the ablations suggest it does contribute, especially on the new benchmark.

The soft spots are real but concentrated in the evaluation protocol. The paper never states the CRAVE-DB train/test split ratio, whether the split is at video or prompt level, or whether the same prompt can appear in both train and test. With only 410 prompts for 1,228 videos, a video-level split will almost certainly leak prompts across sets, and since the MTT module explicitly learns text-video alignment, that leakage could plausibly explain most of the 0.03 SRCC gap over T2VQA on CRAVE-DB. That is a load-bearing measurement issue for the headline claim. Relatedly, Table 4's design choices (combined text injection, hybrid motion, 16 flow frames) were selected with access to test performance, and no error bars are reported anywhere. The zero-shot VideoGenEval ranking in Figure 7 has no ground truth, so it is a demo, not a validation. On the positive side, the T2VQA-DB results use 10-fold cross-validation and still show a small but consistent gain (0.8122 vs. 0.7965 SRCC), which is independent evidence that the architecture is not just overfitting to the new dataset.

My overall take: this is a solid, worthwhile submission for the AIGC VQA audience. The flaws are addressable, not fatal. A careful editor should send it to review, but the referees should insist on the CRAVE-DB split protocol, prompt-disjointness, and error bars before the benchmark claims are accepted. I would cite the benchmark if the data and code actually ship.

Referee Report

4 major / 4 minor

Summary. The paper proposes CRAVE, a no-reference video quality assessment model for text-to-video AIGC content, targeting 'Sora-era' videos with long, detailed prompts and complex motion. CRAVE combines a visual harmony branch built on DOVER, a multi-granularity text-temporal fusion module that aligns paragraph-, phrase-, and word-level text embeddings with temporally adapted video features, and a hybrid motion-fidelity module combining optical flow and action-recognition features. The authors also introduce CRAVE-DB, a new benchmark of 1,228 videos generated by Kling, Vidu, Qingying, and Sora from 410 elaborate prompts, with MOS from 29 annotators. Experiments report state-of-the-art SRCC/PLCC/KRCC on CRAVE-DB and T2VQA-DB, plus a zero-shot ranking experiment on VideoGenEval.

Significance. The problem addressed is timely: existing AIGC VQA datasets and methods are built around short prompts and simple motion, while current video generators produce longer, content-rich videos. The proposed three-branch design is reasonable, and the new benchmark addresses a real gap by using dense prompts and 29 annotators per video, which exceeds ITU recommendations. The T2VQA-DB evaluation with 10-fold cross-validation provides some independent grounding beyond the authors' own dataset. If the evaluation-protocol concerns below are resolved, CRAVE and CRAVE-DB would be a useful contribution to the community. The planned public release of data and code is a further strength.

major comments (4)

[Section 5.1, Table 2] The CRAVE-DB evaluation protocol is not specified at the level needed to validate the reported state-of-the-art results. The text states only that the authors 'train 40 epochs on the training split and evaluate on the test set.' It does not state the split ratio, whether the split is at the video level or the prompt level, or whether any of the 410 prompts appear in both training and test partitions. Because CRAVE's MTT module explicitly learns text-video alignment and CRAVE-DB contains multiple videos per prompt, a random video-level split is likely to place videos sharing a prompt in both partitions. Such prompt leakage can inflate SRCC/PLCC/KRCC, and the margin over T2VQA (0.7587 vs. 0.7266 SRCC) is small enough that leakage could account for part of it. Please specify the exact split and report results under a prompt-disjoint split.
[Section 5.5, Table 4] The ablation study selects the final design ('Combined' text injection, 'Hybrid' motion modeling, 16 flow frames) using test-set performance on CRAVE-DB and T2VQA-DB, and the same test sets are then used for the headline comparisons in Tables 2 and 3. This selection-on-test procedure means the reported margins include model-selection bias. The design choices should be fixed on a validation split (or via nested cross-validation) before evaluating on the test set, or the reported numbers should be adjusted accordingly.
[Tables 2 and 3] No error bars or significance tests are reported. T2VQA-DB is evaluated with 10-fold cross-validation, so mean and standard deviation over folds should be given; for CRAVE-DB, multiple random splits or bootstrapping are needed to establish whether the 0.03-0.04 SRCC advantages over prior methods are statistically meaningful.
[Section 5.4, Figure 7] The zero-shot ranking experiment does not validate '0-shot generalization capability' because VideoGenEval has no human MOS and Figure 7 is presented without any quantitative comparison to human preference. As reported, the figure only shows that CRAVE produces rankings. Either obtain human ratings for a subset of VideoGenEval videos and compute correlation, or reframe the figure as a qualitative demonstration and remove the generalization claim.

minor comments (4)

[Section 5.1] For CRAVE-DB, the training schedule is described only as '40 epochs'; it is unclear whether the linear-probing-then-fine-tuning protocol used for T2VQA-DB also applies. Please state the exact protocol.
[Section 3.2] The sentence 'we curated 14 content-rich prompts and their corresponding outputs from Sora's publicly showcased videos, resulting in a total of 1,228 videos' is ambiguous: does Sora contribute 14 videos or multiple videos per prompt? Please report the number of videos per generation model.
[Section 4.3, Eq. (11)] The summation over granularity levels uses unweighted cosine distances; the ablated variants in Table 4 suggest sensitivity to how levels are combined, so a brief discussion or sensitivity analysis of the combination weights would help.
[Table 2] Several '0-shot' baselines (HPSv2, PickScore, ImageReward) are image preference models rather than video quality models; noting their input modality in the table caption would avoid confusion.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: CRAVE is a supervised MOS regression evaluated on an external benchmark; CRAVE-DB protocol concerns are validity risks, not derivation-equivalence.

full rationale

The core result is empirical: CRAVE is trained with PLCC plus rank loss against human MOS and then tested. On T2VQA-DB, evaluation uses 10-fold cross-validation with a specified protocol and is independent of the proposed components; self-citations such as TriVQA, VE-Bench/IE-Bench, and StreamFlow are comparisons or implementation priors and are not load-bearing. No equation defines the target in terms of the method, and no fitted parameter is renamed as a prediction. The CRAVE-DB rows are the main concern: Section 5.1 states only 'we train 40 epochs on the training split and evaluate on the test set' without giving the split ratio or prompt/video-level disjointness, and Table 4's 'Underlined settings are used in our final model' suggests hyperparameters were selected with access to CRAVE-DB test results. Those are evaluation-validity risks, including possible leakage and selection bias, rather than circularity in the derivation chain. Section 5.4's zero-shot ranking also lacks quantitative human-preference ground truth, so the '0-shot generalization capability' claim is under-supported, but missing evidence is not circular equivalence.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

No invented entities. The free parameters are hand-set hyperparameters, two of which (flow frames, gamma) are not justified by sensitivity analysis. The axioms are domain assumptions about the decomposition of quality and the reliability of the new benchmark.

free parameters (3)

Rank loss coefficient gamma = 0.3
Set in Section 4.5 with no sensitivity study; the training objective mixes PLCC and rank losses with this fixed weight.
Optical flow frame count = 16
Selected in Table 4 because it yields the highest test SRCC/PLCC on both benchmarks; this is test-set-based tuning.
Training epochs and schedule = 20 linear probing + 10 full fine-tuning on T2VQA-DB; 40 epochs on CRAVE-DB
Hyperparameters chosen in Section 5.1 without ablations or validation sweeps.

assumptions (4)

domain assumption AIGC video quality can be decomposed into visual harmony, text-video alignment, and motion consistency.
Section 4.1 defines the three branches and the ablation study only varies components within this decomposition; the decomposition itself is assumed.
domain assumption Pretrained DOVER, BLIP, StreamFlow, and Uniformer features provide sufficient quality, language, and motion priors.
Sections 4.2 to 4.4 use these models as feature extractors; their suitability for Sora-era videos is assumed, not validated.
domain assumption CRAVE-DB prompts and human MOS are representative and reliable.
Section 3.3 describes training and BT.500 screening, but no inter-annotator agreement statistic is reported; the 410 prompts come from curated summaries and manual writing.
standard math Cosine similarity between text and video features measures semantic alignment.
Equation 11 sums cosine distances across granularities; this assumes cosine distance captures alignment without learned calibration.

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Cite this review

Pith. "Pith review of Content-Rich AIGC Video Quality Assessment via Intricate Text Alignment and Motion-Aware Consistency." pith.science (2026). https://pith.science/paper/2N4R4TFM

@misc{pith2026250204076,
  author       = {Pith},
  title        = {Pith review of: Content-Rich AIGC Video Quality Assessment via Intricate Text Alignment and Motion-Aware Consistency},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2N4R4TFM}},
  note         = {Machine review of arXiv:2502.04076}
}

read the original abstract

The advent of next-generation video generation models like \textit{Sora} poses challenges for AI-generated content (AIGC) video quality assessment (VQA). These models substantially mitigate flickering artifacts prevalent in prior models, enable longer and complex text prompts and generate longer videos with intricate, diverse motion patterns. Conventional VQA methods designed for simple text and basic motion patterns struggle to evaluate these content-rich videos. To this end, we propose \textbf{CRAVE} (\underline{C}ontent-\underline{R}ich \underline{A}IGC \underline{V}ideo \underline{E}valuator), specifically for the evaluation of Sora-era AIGC videos. CRAVE proposes the multi-granularity text-temporal fusion that aligns long-form complex textual semantics with video dynamics. Additionally, CRAVE leverages the hybrid motion-fidelity modeling to assess temporal artifacts. Furthermore, given the straightforward prompts and content in current AIGC VQA datasets, we introduce \textbf{CRAVE-DB}, a benchmark featuring content-rich videos from next-generation models paired with elaborate prompts. Extensive experiments have shown that the proposed CRAVE achieves excellent results on multiple AIGC VQA benchmarks, demonstrating a high degree of alignment with human perception. All data and code will be publicly available at https://github.com/littlespray/CRAVE.

Figures

Figures reproduced from arXiv: 2502.04076 by the authors.

**Figure 1.** Comparison of concurrent and previous AIGC videos. Videos are generated by Lavie (Wang et al., 2023c) (1st row) and Sora (Brooks et al., 2024) (2nd row), respectively. Nouns that should be present in the video are highlighted in orange, while adjectives with more details are highlighted in blue. The new-generation AIGC videos contain richer content. sues prevalent in previous generations, such as flickering, weak mo… view at source ↗

**Figure 2.** Word cloud of prompts in CRAVE-DB. 3.1. Prompt Collection The past AIGC VQA datasets were composed of previousgeneration models, where most supported prompt length is limited by CLIP (Radford et al., 2021). In this case, these prompts tend to be brief, making it challenging to incorporate complex motion descriptions and scene compositions. For instance, we present the prompt density (average word and character cou… view at source ↗

**Figure 4.** Distribution of MOS in CRAVE-DB. 3.2. Video Generation Since the advent of Sora (Brooks et al., 2024), text-driven video generation methods have achieved significant advancements in visual quality, text understanding, and the diversity and complexity of generated content. Given the substantial gap between current AIGC videos and prior ones, constructing datasets using the next-generation video models is essential… view at source ↗

Figures from the paper (4 more)

**Figure 5.** Figure 5: Network overview of the proposed CRAVE. to scoring, all participants were gathered on-site for training. During the training, we presented some cases outside the dataset, including good, bad, and average examples, to ensure a basic understanding of the task. The scori…

**Figure 6.** Figure 6: Details of the proposed MTT module for text alignment. 4. Content-Rich AIGC Video Evaluator 4.1. Overall Framework CRAVE evaluates content-rich AIGC videos from three perspectives: (1) visual harmony, measured using traditional video quality metrics like aesthetics an…

**Figure 7.** Figure 7: The ranking of next-generation models provided by models trained on different AIGC VQA datasets [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]

**Figure 8.** Figure 8: Scatter plots of the predicted scores and ground-truth MOSs. A brighter scatter point represents higher density. As shown in [PITH_FULL_IMAGE:figures/full_fig_p013_8.png]

Discussion (0). Continue with ORCID to comment.

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