Towards World Simulator: Crafting Physical Commonsense-Based Benchmark
  for Video Generation
Authors: name: Fanqing Meng, name: Jiaqi Liao, name: Xinyu Tan, name: Wenqi Shao, name: Quanfeng Lu, name: Kaipeng Zhang, name: Yu Cheng, name: Dianqi Li, name: Yu Qiao, name: Ping Luo

Text-to-video (T2V) models like Sora have made significant strides in
visualizing complex prompts, which is increasingly viewed as a promising path
towards constructing the universal world simulator. Cognitive psychologists
believe that the foundation for achieving this goal is the ability to
understand intuitive physics. However, the capacity of these models to
accurately represent intuitive physics remains largely unexplored. To bridge
this gap, we introduce PhyGenBench, a comprehensive Physics
Generation Benchmark designed to evaluate physical
commonsense correctness in T2V generation. PhyGenBench comprises 160 carefully
crafted prompts across 27 distinct physical laws, spanning four fundamental
domains, which could comprehensively assesses models' understanding of physical
commonsense. Alongside PhyGenBench, we propose a novel evaluation framework
called PhyGenEval. This framework employs a hierarchical evaluation structure
utilizing appropriate advanced vision-language models and large language models
to assess physical commonsense. Through PhyGenBench and PhyGenEval, we can
conduct large-scale automated assessments of T2V models' understanding of
physical commonsense, which align closely with human feedback. Our evaluation
results and in-depth analysis demonstrate that current models struggle to
generate videos that comply with physical commonsense. Moreover, simply scaling
up models or employing prompt engineering techniques is insufficient to fully
address the challenges presented by PhyGenBench (e.g., dynamic scenarios). We
hope this study will inspire the community to prioritize the learning of
physical commonsense in these models beyond entertainment applications. We will
release the data and codes at https://github.com/OpenGVLab/PhyGenBench