Overview:

✔️ T2V-CompBench: We conduct the first systematic study on compositional text-to-video generation and propose this benchmark.
✔️ 1400 Prompts: We analyze 1.67 million real-user prompts to extract high-frequency nouns, verbs, and adjectives, resulting in 1,400 prompts.
✔️ 7 Categories: We evaluate multiple-object compositionality on attributes, 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 benchmark 20+ text-to-video generation models, provide insightful analysis on current models' ability, highlighting the significant challenge of compositional T2V generation.

Introduction

 

 

Evaluation Metrics

MLLM-based evaluation metrics for consistent and dynamic attribute binding, action binding and object interactions.
Detection-based evaluation metrics for spatial relationships and object interactions.
Tracking-based evaluation metrics for motion binding.

 

Evaluation Results

 

 

Bibtex

    @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}
     }