The burgeoning field of Artificial Intelligence Generated Content (AIGC) is witnessing rapid advancements, particularly in video generation. This paper introduces AIGCBench, a pioneering comprehensive and scalable benchmark designed to evaluate a variety of video generation tasks, with a primary focus on Image-to-Video (I2V) generation. AIGCBench tackles the limitations of existing benchmarks, which suffer from a lack of diverse datasets, by including a varied and open-domain image-text dataset that evaluates different state-of-the-art algorithms under equivalent conditions. We employ a novel text combiner and GPT-4 to create rich text prompts, which are then used to generate images via advanced Text-to-Image models. To establish a unified evaluation framework for video generation tasks, our benchmark includes 11 metrics spanning four dimensions to assess algorithm performance. These dimensions are control-video alignment, motion effects, temporal consistency, and video quality. These metrics are both reference video-dependent and video-free, ensuring a comprehensive evaluation strategy. The evaluation standard proposed correlates well with human judgment, providing insights into the strengths and weaknesses of current I2V algorithms. The findings from our extensive experiments aim to stimulate further research and development in the I2V field. AIGCBench represents a significant step toward creating standardized benchmarks for the broader AIGC landscape, proposing an adaptable and equitable framework for future assessments of video generation tasks.

翻译：人工智能生成内容（AIGC）领域正在快速发展，尤其在视频生成方面。本文介绍了AIGCBench，这是一个开创性的、全面的、可扩展的基准测试，旨在评估多种视频生成任务，主要聚焦于图像到视频（I2V）生成。AIGCBench通过包含多样化的开放域图像-文本数据集，在同等条件下评估不同最先进算法，解决了现有基准测试因数据集缺乏多样性而存在的局限性。我们使用新颖的文本组合器和GPT-4创建丰富的文本提示，随后通过先进的文本到图像模型生成图像。为了建立统一的视频生成任务评估框架，我们的基准测试包含跨越四个维度的11个指标，用于评估算法性能。这些维度分别是：控制-视频对齐、运动效果、时间一致性和视频质量。这些指标既包括依赖参考视频的指标，也包括不依赖参考视频的指标，从而确保评估策略的全面性。所提出的评估标准与人工判断高度一致，为当前I2V算法的优势与不足提供了深刻见解。我们广泛实验的发现旨在推动I2V领域的进一步研究与发展。AIGCBench为更广泛的AIGC领域创建标准化基准迈出了重要一步，并为未来视频生成任务的评估提出了一个适应性强且公平的框架。