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ChronoMagic-Bench: A Benchmark for Metamorphic Evaluation of Text-to-Time-lapse Video Generation

26 Jun 2024arXiv:2406.18522archive 2025-07-28

Shenghai Yuan, Jinfa Huang, Yongqi Xu, Yaoyang Liu, Shaofeng Zhang, Yujun Shi, Ruijie Zhu, Xinhua Cheng, Jiebo Luo, Li Yuan

We propose a novel text-to-video (T2V) generation benchmark, ChronoMagic-Bench, to evaluate the temporal and metamorphic capabilities of the T2V models (e.g. Sora and Lumiere) in time-lapse video generation. In contrast to existing benchmarks that focus on visual quality and textual relevance of generated videos, ChronoMagic-Bench focuses on the model's ability to generate time-lapse videos with significant metamorphic amplitude and temporal coherence. The benchmark probes T2V models for their physics, biology, and chemistry capabilities, in a free-form text query. For these purposes, ChronoMagic-Bench introduces 1,649 prompts and real-world videos as references, categorized into four major types of time-lapse videos: biological, human-created, meteorological, and physical phenomena, which are further divided into 75 subcategories. This categorization comprehensively evaluates the model's capacity to handle diverse and complex transformations. To accurately align human preference with the benchmark, we introduce two new automatic metrics, MTScore and CHScore, to evaluate the videos' metamorphic attributes and temporal coherence. MTScore measures the metamorphic amplitude, reflecting the degree of change over time, while CHScore assesses the temporal coherence, ensuring the generated videos maintain logical progression and continuity. Based on ChronoMagic-Bench, we conduct comprehensive manual evaluations of ten representative T2V models, revealing their strengths and weaknesses across different categories of prompts, and providing a thorough evaluation framework that addresses current gaps in video generation research. Moreover, we create a large-scale ChronoMagic-Pro dataset, containing 460k high-quality pairs of 720p time-lapse videos and detailed captions ensuring high physical pertinence and large metamorphic amplitude. [Homepage](https://pku-yuangroup.github.io/ChronoMagic-Bench/).

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calculate_average_score pku-yuangroup/chronomagic-bench/MTScore/step0-get_MTScore.py official repository ran · violated contract Apache-2.0 (permissive) · 8204634e93f03f47 · report
calculate_average_scores PKU-YuanGroup/ChronoMagic-Bench/UMT/step3_get_merge_umt_scores.py official repository ran fingerprinted Apache-2.0 (permissive) · 5ce38342e526b34f · report
create_prompts pku-yuangroup/magictime/data_preprocess/step3_1_GPT4V_video_caption_concise.py official repository ran Apache-2.0 (permissive) · be4269f1f4abf08e · report
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get_image_filenames PKU-YuanGroup/ChronoMagic-Bench/GPT4o_MTScore/step1-get_temp_results.py official repository ran Apache-2.0 (permissive) · 88a50570825e6b77 · report
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Tasks

Text-to-Video GenerationVideo Generation

Datasets

Introduced by this paper, per the archive.

ChronoMagic-ProChronoMagic-ProH

Results from the paper archive 2025-07-28

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Methods

ALIGNFocus

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