"PhyWorldBench": A Comprehensive Evaluation of Physical Realism in
  Text-to-Video Models
Authors: name: Jing Gu, name: Xian Liu, name: Yu Zeng, name: Ashwin Nagarajan, name: Fangrui Zhu, name: Daniel Hong, name: Yue Fan, name: Qianqi Yan, name: Kaiwen Zhou, name: Ming-Yu Liu, name: Xin Eric Wang

Video generation models have achieved remarkable progress in creating
high-quality, photorealistic content. However, their ability to accurately
simulate physical phenomena remains a critical and unresolved challenge. This
paper presents PhyWorldBench, a comprehensive benchmark designed to evaluate
video generation models based on their adherence to the laws of physics. The
benchmark covers multiple levels of physical phenomena, ranging from
fundamental principles like object motion and energy conservation to more
complex scenarios involving rigid body interactions and human or animal motion.
Additionally, we introduce a novel ""Anti-Physics"" category, where prompts
intentionally violate real-world physics, enabling the assessment of whether
models can follow such instructions while maintaining logical consistency.
Besides large-scale human evaluation, we also design a simple yet effective
method that could utilize current MLLM to evaluate the physics realism in a
zero-shot fashion. We evaluate 12 state-of-the-art text-to-video generation
models, including five open-source and five proprietary models, with a detailed
comparison and analysis. we identify pivotal challenges models face in adhering
to real-world physics. Through systematic testing of their outputs across 1,050
curated prompts-spanning fundamental, composite, and anti-physics scenarios-we
identify pivotal challenges these models face in adhering to real-world
physics. We then rigorously examine their performance on diverse physical
phenomena with varying prompt types, deriving targeted recommendations for
crafting prompts that enhance fidelity to physical principles.