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The vision and language generative models have been overgrown in recentyears. For video generation, various open-sourced models and public-availableservices are released for generating high-visual quality videos. However, thesemethods often use a few academic metrics, for example, FVD or IS, to evaluatethe performance. We argue that it is hard to judge the large conditionalgenerative models from the simple metrics since these models are often trainedon very large datasets with multi-aspect abilities. Thus, we propose a newframework and pipeline to exhaustively evaluate the performance of thegenerated videos. To achieve this, we first conduct a new prompt list fortext-to-video generation by analyzing the real-world prompt list with the helpof the large language model. Then, we evaluate the state-of-the-art videogenerative models on our carefully designed benchmarks, in terms of visualqualities, content qualities, motion qualities, and text-caption alignment witharound 18 objective metrics. To obtain the final leaderboard of the models, wealso fit a series of coefficients to align the objective metrics to the users'opinions. Based on the proposed opinion alignment method, our final score showsa higher correlation than simply averaging the metrics, showing theeffectiveness of the proposed evaluation method.

EvalCrafter: Benchmarking and Evaluating Large Video Generation Models