Intuitive physics is pivotal for human understanding of the physical world,
enabling prediction and interpretation of events even in infancy. Nonetheless,
replicating this level of intuitive physics in artificial intelligence (AI)
remains a formidable challenge. This study introduces X-VoE, a comprehensive
benchmark dataset, to assess AI agents' grasp of intuitive physics. Built on
the developmental psychology-rooted Violation of Expectation (VoE) paradigm,
X-VoE establishes a higher bar for the explanatory capacities of intuitive
physics models. Each VoE scenario within X-VoE encompasses three distinct
settings, probing models' comprehension of events and their underlying
explanations. Beyond model evaluation, we present an explanation-based learning
system that captures physics dynamics and infers occluded object states solely
from visual sequences, without explicit occlusion labels. Experimental outcomes
highlight our model's alignment with human commonsense when tested against
X-VoE. A remarkable feature is our model's ability to visually expound VoE
events by reconstructing concealed scenes. Concluding, we discuss the findings'
implications and outline future research directions. Through X-VoE, we catalyze
the advancement of AI endowed with human-like intuitive physics capabilities.

本研究介绍了 X-VoE，一个全面基准数据集，用于评估人工智能代理对直观物理的理解。在 X-VoE 中，每个 VoE 场景包含三个不同的设置，探测模型对事件及其潜在解释的理解能力。通过 X-VoE 的实验结果显示，我们的模型在与人类常识进行测试时具有很好的一致性，并能通过重建隐藏场景来解释 VoE 事件。通过 X-VoE，我们推动了人类直观物理能力的人工智能发展。