Retracted: T2VBench: Benchmarking Temporal Dynamics for Text-to-Video Generation
Why this work is in the frame
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Post-publication record
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Bibliographic record
Abstract
While text-to-video (T2V) generative models produce exceptionally realistic videos, they lack a comprehensive evaluation across the temporal dimension, with a limited focus on basic dynamics including camera transitions, movement, and event sequences. In this work, we introduce T2VBench, a comprehensive T2V evaluation benchmark enriched with temporal dynamics lexicons derived from curated temporal words on Wikipedia. T2VBench is a hierarchical evaluation framework comprising over 1,600 temporally rich prompts and 5,000 generated videos with human ratings, spanning 16 critical temporal evaluation dimensions. We assess three leading text-to-video models, including ZeroScope and Pika, to gauge their proficiency in handling temporal dynamics. Our analysis highlights the strengths and limitations of these models across various temporal aspects. Furthermore, we provide insights into future directions for enhancing text-to-video evaluation metrics and offer a detailed analysis of these models’ performance across the temporal dimensions. Overall, T2VBench is the first-of-its-kind comprehensive benchmark fully focused on temporal dynamics for text-to-video evaluation. It aims to facilitate scientific benchmarking of both generative models and automated metrics on text-to-video generation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it