T2V-CompBench: A Comprehensive Benchmark for Compositional Text-to-video Generation
<|reference_start|>T2V-CompBench: A Comprehensive Benchmark for Compositional Text-to-video Generation: Text-to-video (T2V) generation models have advanced significantly, yet their ability to compose different objects, attributes, actions, and motions into a video remains unexplored. Previous text-to-video benchmarks also neglect this important ability for evaluation. In this work, we conduct the first systematic study on compositional text-to-video generation. We propose T2V-CompBench, the first benchmark tailored for compositional text-to-video generation. T2V-CompBench encompasses diverse aspects of compositionality, including consistent attribute binding, dynamic attribute binding, spatial relationships, motion binding, action binding, object interactions, and generative numeracy. We further carefully design evaluation metrics of MLLM-based metrics, detection-based metrics, and tracking-based metrics, which can better reflect the compositional text-to-video generation quality of seven proposed categories with 700 text prompts. The effectiveness of the proposed metrics is verified by correlation with human evaluations. We also benchmark various text-to-video generative models and conduct in-depth analysis across different models and different compositional categories. We find that compositional text-to-video generation is highly challenging for current models, and we hope that our attempt will shed light on future research in this direction.<|reference_end|>
@article{sun2024t2v-compbench:,
    title={T2V-CompBench: A Comprehensive Benchmark for Compositional Text-to-video
  Generation},
    author={Kaiyue Sun, Kaiyi Huang, Xian Liu, Yue Wu, Zihan Xu, Zhenguo Li, Xihui
  Liu},
    journal={arXiv preprint arXiv:2407.14505},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.14505},
    primaryClass={cs.CV}
}