constants.py

import os
# this is .py for store constants 
MODEL_INFO = [
    "Model Name (clickable)",
    "Evaluated by",
    "Date",
    "Total Avg. Score",
    "Selected Avg. Score",
    ]

TASK_INFO = [
    "Consistent Attribute Binding",
    "Dynamic Attribute Binding",
    "Spatial Relationships",
    "Motion Binding",
    "Action Binding",
    "Object Interactions",
    "Generative Numeracy",
    ]

SUB_TASK_INFO = [
    "Consistent Attribute Binding-Color",
    "Consistent Attribute Binding-Shape",
    "Consistent Attribute Binding-Texture",
    "2D Spatial Relationships-Coexist",
    "2D Spatial Relationships-Acc.",
    "2D Spatial Relationships-Acc.Score",
    "Motion Binding-Motion Level",
    "Motion Binding-Acc.",
    "Action Binding-Common",
    "Action Binding-Uncommon",
    "Object Interactions-Physical",
    "Object Interactions-Social",
]



SUBMISSION_NAME = "T2V-CompBench_leaderboard_submission"
SUBMISSION_URL = os.path.join("https://huggingface.co/datasets/Kaiyue/", SUBMISSION_NAME)
CSV_PATH = "./T2V-CompBench_leaderboard_submission/results.csv"
INFO_PATH = "./T2V-CompBench_leaderboard_submission/model_info.csv"



COLUMN_NAMES = MODEL_INFO + TASK_INFO
DATA_TITLE_TYPE = ['markdown', 'markdown', 'markdown', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number','number']

SUB_COLUMN_NAMES = MODEL_INFO + SUB_TASK_INFO
SUB_DATA_TITLE_TYPE = ['markdown', 'markdown', 'markdown', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number','number', 'number','number', 'number','number', 'number']


LEADERBOARD_INTRODUCTION  = """# T2V-CompBench Leaderboard
     
    🏆 Welcome to the leaderboard of the **T2V-CompBench**! 🎦 *A Comprehensive Benchmark for Compositional Text-to-video Generation*  """

LEADERBOARD_INTRODUCTION_HTML = """
    <div style="display: flex; flex-wrap: wrap; align-items: center; gap: 10px;">
    <a href='https://github.com/KaiyueSun98/T2V-CompBench/tree/V2'><img src='https://img.shields.io/github/stars/KaiyueSun98/T2V-CompBench.svg?style=social&label=Official'></a>
    <a href='https://arxiv.org/abs/2407.14505'><img src='https://img.shields.io/badge/cs.CV-Paper-b31b1b?logo=arxiv&logoColor=red'></a>
    <a href="https://t2v-compbench-2025.github.io"><img src="https://img.shields.io/badge/Project-Page-Green"></a>
    </div>
"""
LEADERBOARD_INTRODUCTION_2  = """
    - **1400 Prompts**: We analyze *1.67 million* real-user prompts to extract high-frequency nouns, verbs, and adjectives, resulting in a suite of 1,400 prompts.
    - **7 Compositional Categories:** We evaluate multiple-object compositionality on attributes, actions, interactions, quantities, and spatio-temporal dynamics, covering 7 categories.
    - **Evaluation metrics**: We design MLLM-based, Detection-based, and Tracking-based evaluation metrics for compositional T2V generation, all validated by human evaluations. 
    - **Valuable Insights:** We provide insightful analysis on current models' ability, highlighting the significant challenge of compositional T2V generation.  
    
    **Join Leaderboard**: Please follow the steps in [our github repository](https://github.com/KaiyueSun98/T2V-CompBench/tree/V2) to prepare the videos and run the evaluation scripts. Before uploading the generated `.csv` files here, please conduct a final check by carefully reading this [instruction](https://github.com/KaiyueSun98/T2V-CompBench/tree/V2?tab=readme-ov-file#mortar_board-how-to-join-t2v-compbench-leaderboard).  After clicking the `Submit Eval!` button, click the `Refresh` button. Then, you can successfully showcase your model's performance on our leaderboard!

    **Model Information**: What are the details of these Video Generation Models? See Appendix B of [our paper](https://arxiv.org/abs/2407.14505). We will provide more details soon.
    """
    
SUBMIT_INTRODUCTION = """# Submit on T2V-CompBench Introduction
## 📮
1. Please note that you need to obtain a list of `.csv` files by running the evaluation scripts of T2V-CompBench in our Github. You may conduct an [Offline Check](https://github.com/KaiyueSun98/T2V-CompBench/tree/V2?tab=readme-ov-file#mortar_board-how-to-join-t2v-compbench-leaderboard) before uploading.
2. Then, pack these CSV files into a `ZIP` archive, ensuring that the top-level directory of the ZIP contains the individual CSV files. 
3. Finally, upload the ZIP archive below.

⚠️ Uploading generated videos of the model is invalid!

⚠️ Submissions that do not correctly fill in the model name and model link may be deleted by the T2V-CompBench team. The contact information you filled in will not be made public. 
"""

LEADERBOARD_INFO = """
- T2V-CompBench, a comprehensive benchmark for compositional text-to-video generation, consists of seven categories: **consistent attribute binding, dynamic attribute binding, spatial relationships, motion binding, action binding, object interactions, and generative numeracy**. 
- For each category, we carefully design 200 prompts, resulting in **1400** in total, and sample generated videos from a set of T2V models. 
- We propose three types of evaluation metrics: **MLLM-based, Detection-based, and Tracking-based metrics**, all specifically designed for compositional T2V generation and validated by human evaluations. 
- We benchmark various T2V models, reveal their strengths and weaknesses by examining the results across **7 categories and 12 sub-dimensions**, meanwhile provide insightful analysis on compositional T2V generation.
"""

CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
CITATION_BUTTON_TEXT = r"""@article{sun2024t2v,
  title={T2v-compbench: A comprehensive benchmark for compositional text-to-video generation},
  author={Sun, Kaiyue and Huang, Kaiyi and Liu, Xian and Wu, Yue and Xu, Zihan and Li, Zhenguo and Liu, Xihui},
  journal={arXiv preprint arXiv:2407.14505},
  year={2024}
}"""