ContPhy: Continuum Physical Concept Learning and Reasoning from Videos
Zhicheng Zheng Xin Yan Zhenfang Chen Jingzhou Wang Qin Zhi Eddie Lim Joshua B. Tenenbaum Chuang Gan
We introduce the Continuum Physical Dataset (ContPhy), a novel benchmark for
assessing machine physical commonsense. ContPhy complements existing physical
reasoning benchmarks by encompassing the inference of diverse physical
properties, such as mass and density, across various scenarios and predicting
corresponding dynamics. We evaluated a range of AI models and found that they
still struggle to achieve satisfactory performance on ContPhy, which shows that
the current AI models still lack physical commonsense for the continuum,
especially soft-bodies, and illustrates the value of the proposed dataset. We
also introduce an oracle model (ContPRO) that marries the particle-based
physical dynamic models with the recent large language models, which enjoy the
advantages of both models, precise dynamic predictions, and interpretable
reasoning. ContPhy aims to spur progress in perception and reasoning within
diverse physical settings, narrowing the divide between human and machine
intelligence in understanding the physical world. Project page:
https://physical-reasoning-project.github.io.