Atomic Physical Transitions: A Structured Representation for Physical Dynamics in Video

Current video understanding often compresses multiple physical processes into clip-level labels or answers, allowing correct event-level predictions without explicitly recovering the underlying state changes. These processes can instead be represented as compositions of reusable causal units—physical state transitions defined by the mechanisms that drive them rather than by scene appearance. We introduce Atomic Physical Transitions (APTs): minimal, temporally localized state changes that bind visual evidence to an active mechanism and a before/after physical state, forming an ordered transition chain for each video. For diagnosis and supervision, we construct APT-BENCH, covering 14 transition types across contact, gravity, friction, and rotation/stability, with approximately 55,000 timed instances over 2,500 trials. A human-anchored, rule-guided simulation pipeline scales annotation using realized state traces rather than intended scene templates. Evaluation of twelve vision-language models reveals low zero-shot transition recovery, and a denser-input check shows that the recovery gap persists. APT-only fine-tuning improves detection but can degrade event-level answering. We therefore introduce APT-Tune, a parameter-efficient recipe combining multimodal answer-only masking with format-conditional co-training across APT, multiple-choice, and description targets. Across six open-source backbones, it substantially improves transition recovery while retaining event-level performance on MVBench. APT-Tune consistently improves performance on PhysBench across the evaluated backbones. These results support APTs as an intermediate representation between coarse event recognition and full physical simulation, useful for both diagnosis and transferable supervision. The project page is at https://apt-physical-transitions-2027.netlify.app/