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MODEL_INFO = ["Model", "Backbone"] | |
ALL_RESULTS = ["UMT-FVD↓", "UMTScore↑", "MTScore↑", "CHScore↑", "GPT4o-MTScore↑"] | |
SELECTED_RESULTS = ["UMT-FVD↓", "UMTScore↑", "MTScore↑", "CHScore↑", "GPT4o-MTScore↑"] | |
SELECTED_RESULTS_150 = ["UMT-FVD↓", "UMTScore↑", "MTScore↑", "CHScore↑", "GPT4o-MTScore↑"] | |
DATA_TITILE_TYPE = ["markdown", 'markdown', "number", "number", "number", "number", "number"] | |
CSV_DIR_CHRONOMAGIC_BENCH = "./file/results_ChronoMagic-Bench.csv" | |
CSV_DIR_CHRONOMAGIC_BENCH_150 = "./file/results_ChronoMagic-Bench-150.csv" | |
COLUMN_NAMES = MODEL_INFO + ALL_RESULTS | |
LEADERBORAD_INTRODUCTION = """ | |
<div style='display: flex; align-items: center; justify-content: center; text-align: center;'> | |
<img src='https://www.pnglog.com/MqiNJ0.jpg' style='width: 600px; height: auto; margin-right: 10px;' /> | |
</div> | |
# ChronoMagic-Bench Leaderboard | |
Welcome to the leaderboard of the ChronoMagic-Bench! (**NeurIPS 2024 D&B Spotlight**) | |
🏆ChronoMagic-Bench represents the inaugural benchmark dedicated to assessing T2V models' capabilities in generating time-lapse videos that demonstrate significant metamorphic amplitude and temporal coherence. The benchmark probes T2V models for their physics, biology, and chemistry capabilities, in a free-form text control. | |
If you like our project, please give us a star ⭐ on GitHub for the latest update. | |
[GitHub](https://github.com/PKU-YuanGroup/ChronoMagic-Bench) | [arXiv](https://arxiv.org/abs/2406.18522) | [Home Page](https://pku-yuangroup.github.io/ChronoMagic-Bench/) | [ChronoMagic-Pro](https://huggingface.co/datasets/BestWishYsh/ChronoMagic-Pro) | [ChronoMagic-ProH](https://huggingface.co/datasets/BestWishYsh/ChronoMagic-ProH) | |
""" | |
SUBMIT_INTRODUCTION = """# Submit Introduction | |
Obtain `ChronoMagic-Bench-Input.json` from our [github repository](https://github.com/PKU-YuanGroup/ChronoMagic-Bench) after evaluation. | |
## Submit Example | |
For example, if you want to upload Video-ChatGPT's result in the leaderboard, you need to: | |
1. Fill in 'MagicTime' in 'Model Name' if it is your first time to submit your result (You can leave 'Revision Model Name' blank). | |
2. Fill in 'MagicTime' in 'Revision Model Name' if you want to update your result (You can leave 'Model Name' blank). | |
3. Select ‘Backbone Type’ (DiT or U-Net). | |
4. Fill in 'https://github.com/x/x' in 'Model Link'. | |
5. Upload `ChronoMagic-Bench-Input.json`. | |
6. Click the 'Submit Eval' button. | |
7. Click 'Refresh' to obtain the uploaded leaderboard. | |
""" | |
TABLE_INTRODUCTION = """In the table below, we summarize each task performance of all the models. | |
We use UMT-FVD, UMTScore, MTScore, CHScore, GPT4o-MTScore as the primary evaluation metric for each tasks. | |
""" | |
CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results" | |
CITATION_BUTTON_TEXT = r"""@article{yuan2024magictime, | |
title={MagicTime: Time-lapse Video Generation Models as Metamorphic Simulators}, | |
author={Yuan, Shenghai and Huang, Jinfa and Shi, Yujun and Xu, Yongqi and Zhu, Ruijie and Lin, Bin and Cheng, Xinhua and Yuan, Li and Luo, Jiebo}, | |
journal={arXiv preprint arXiv:2404.05014}, | |
year={2024} | |
}""" | |