"PhyWorldBench": A Comprehensive Evaluation of Physical Realism in
  Text-to-Video Models
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.
687f4a5933947f780d9b4bb9
Jing Gu
687f4a5933947f780d9b4bba
Xian Liu
687f4a5933947f780d9b4bbb
Yu Zeng
687f4a5933947f780d9b4bbc
Ashwin Nagarajan
687f4a5933947f780d9b4bbd
Fangrui Zhu
687f4a5933947f780d9b4bbe
Daniel Hong
687f4a5933947f780d9b4bbf
Yue Fan
687f4a5933947f780d9b4bc0
Qianqi Yan
687f4a5933947f780d9b4bc1
Kaiwen Zhou
687f4a5933947f780d9b4bc2
Ming-Yu Liu
687f4a5933947f780d9b4bc3
Xin Eric Wang
claimed_verified
2025-07-24T09:13:38.465Z
64679a226192d39142245e5e
/avatars/05abee0b6317f100923936ca2099e9eb.svg
Xin Eric Wang
user
xw-eric
"PhyWorldBench": A Comprehensive Evaluation of Physical Realism in
  Text-to-Video Models
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.
PhyWorldBench evaluates video generation models' adherence to physical laws through a comprehensive benchmark, including an "Anti-Physics" category, and provides recommendations for improving physical realism.
PhyWorldBench video generation models physical phenomena object motion energy conservation rigid body interactions human motion Anti-Physics MLLM zero-shot evaluation physics realism text-to-video generation models fidelity to physical principles