LivePhys: Transforming Static Physics Problems into Interactive Simulations via a Scan-to-Play Framework
TL;DR
Results show that LivePhys significantly outperforms general-purpose multimodal models in simulation executability, spatial accuracy, and interaction fidelity, and a user study demonstrates that interacting with LivePhys-generated simulations reduces learners' perceived cognitive load compared to static textbook materials.
Abstract
Physics problems in textbooks are typically presented as static diagrams accompanied by brief textual descriptions, requiring learners to infer dynamic physical behaviors through mental visualization. This process often imposes high cognitive demands and limits learners'ability to form accurate mental models. In this paper, we present \textbf{LivePhys}, a framework that enables a \emph{Scan-to-Play} paradigm for mechanics learning by transforming static textbook physics problems into executable, interactive simulations. LivePhys decouples multimodal perception from physics-aware reasoning and deterministic simulation. Given a problem diagram and its accompanying text, LivePhys performs text extraction, geometric segmentation, and cross-modal grounding to construct a structured, physics-aware intermediate representation. A multimodal large language model is then used as a reasoning controller to infer entities, parameters, and constraints, which are executed by a physics engine to generate spatially consistent and interactive simulations that allow learners to explore and manipulate problem conditions dynamically. Our evaluation results show that LivePhys significantly outperforms general-purpose multimodal models in simulation executability, spatial accuracy, and interaction fidelity. In addition, a user study demonstrates that interacting with LivePhys-generated simulations reduces learners'perceived cognitive load compared to static textbook materials.