High-quality video generation, encompassing text-to-video (T2V), image-to-video (I2V), and video-to-video (V2V) generation, holds considerable significance in content creation to benefit anyone express their inherent creativity in new ways and world simulation to modeling and understanding the world. Models like SORA have advanced generating videos with higher resolution, more natural motion, better vision-language alignment, and increased controllability, particularly for long video sequences. These improvements have been driven by the evolution of model architectures, shifting from UNet to more scalable and parameter-rich DiT models, along with large-scale data expansion and refined training strategies. However, despite the emergence of DiT-based closed-source and open-source models, a comprehensive investigation into their capabilities and limitations remains lacking. Furthermore, the rapid development has made it challenging for recent benchmarks to fully cover SORA-like models and recognize their significant advancements. Additionally, evaluation metrics often fail to align with human preferences.

翻译：高质量视频生成（涵盖文本到视频、图像到视频及视频到视频生成）在内容创作与世界模拟领域具有重大意义：前者助力人们以全新方式表达内在创造力，后者则为世界建模与理解提供支撑。以SORA为代表的模型在生成更高分辨率、更自然运动、更优视觉-语言对齐及更强可控性的视频方面取得显著进展，尤其在生成长序列视频时表现突出。这些进步得益于模型架构的演进——从UNet转向更具可扩展性与参数规模的DiT模型，以及大规模数据扩展与精细化训练策略的推动。然而，尽管基于DiT的闭源与开源模型相继涌现，学界仍缺乏对其能力与局限性的系统研究。此外，该领域的快速发展使得现有基准测试难以全面覆盖SORA类模型并准确评估其重大进展。同时，现有评估指标往往与人类主观偏好存在偏差。