@inproceedings{
zhang2024rethinking,
title={Rethinking Human Evaluation Protocol for Text-to-Video Models: Enhancing Reliability, Reproducibility, and Practicality},
author={Tianle Zhang and Langtian Ma and Yuchen Yan and Yuchen Zhang and Yue Yang and Ziyao Guo and Wenqi Shao and Kai Wang and Yang You and Yu Qiao and Ping Luo and Kaipeng Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=0AwMciNShl}
}
Recent text-to-video (T2V) technology advancements, as demonstrated by models such as Gen2, Pika, and Sora, have significantly broadened its applicability and popularity. 
Despite these strides, evaluating these models poses substantial challenges. 
Primarily, due to the limitations inherent in automatic metrics, manual evaluation is often considered a superior method for assessing T2V generation. However, existing manual evaluation protocols face reproducibility, reliability, and practicality issues.
To address these challenges, this paper introduces the Text-to-Video Human Evaluation (T2VHE) protocol, a comprehensive and standardized protocol for T2V models. 
The T2VHE protocol includes well-defined metrics, thorough annotator training, and an effective dynamic evaluation module. 
Experimental results demonstrate that this protocol not only ensures high-quality annotations but can also reduce evaluation costs by nearly 50\%.
We will open-source the entire setup of the T2VHE protocol, including the complete protocol workflow, the dynamic evaluation component details, and the annotation interface code. This will help communities establish more sophisticated human assessment protocols.
Rethinking Human Evaluation Protocol for Text-to-Video Models: Enhancing Reliability, Reproducibility, and Practicality
Tianle Zhang Langtian Ma Yuchen Yan Yuchen Zhang Yue Yang Ziyao Guo Wenqi Shao Kai Wang Yang You Yu Qiao Ping Luo Kaipeng Zhang