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.

人工智能生成内容（AIGC）领域迅速发展，本研究介绍了 AIGCBench，一个全面且可扩展的基准测试，旨在评估各种视频生成任务，主要集中在图像到视频（I2V）生成上。