Physics-IQ Verified
For researchers evaluating physical understanding in video generative models, this work provides a more reliable benchmark by fixing confounding factors in the original Physics-IQ.
The authors audited the Physics-IQ benchmark for video generative models, identified shortcomings, and proposed improvements including better prompts, ground-truth quality, and sample-level scoring. The refined benchmark, Physics-IQ Verified, improves 57.6% of samples and 34.8% of prompts, leading to moderate ranking changes (Kendall's τ=0.46) across six models.
Video generative models ( VGMs) have become a new frontier that can be used not just for video generation but for a multitude of downstream tasks, including world modeling. To advance these tasks, a good video model must understand the physical reality of the world. Evaluating this understanding is an emerging field and has led to the Physics-IQ benchmark, which quantifies this explicitly by comparing model-generated videos to real-world videos of physical experiments. In this work, we present a systematic audit of the Physics-IQ benchmark, expose shortcomings and propose three solutions that sharpen how we can measure physical understanding of VGMs. Specifically, we improve prompt and ground-truth quality to reduce the influence of confounding factors and further introduce a sample-level scoring system that weights each sample and metric equally. Our resulting benchmark, Physics-IQ Verified, refines 57.6\% of all samples and improves over 34.8\% of prompts. In a comparison study using six image-to-video generative models, we observe moderate but meaningful ranking changes (Kendall's $τ= 0.46$). We hope Physics-IQ Verified advances the community by providing a more reliable signal toward physically accurate VGMs. The code for the benchmark can be accessed at https://github.com/google-deepmind/physics-iq-benchmark