PHASEPLAY: CONTROLLABLE 3D SCENE SYNTHESIS WITH THERMAL-DRIVEN PHASE TRANSITIONS

Generating physically grounded videos of material transitions remains a significant challenge because both the geometry and appearance of objects evolve dynamically over time. Current approaches often face a trade-off: pre-trained diffusion models provide realistic appearance but frequently suffer from physical hallucinations and offer limited controllability for users, while traditional physics-based simulators excel at motion but struggle to capture complex visual state changes. To address this, we introduce PhasePlay, a framework for action-conditioned 3D scene dynamics that models realistic phase transitions by integrating specialized Material Point Method (MPM) and Position-Based Dynamics (PBD) solvers with an AI-assisted reasoning module. Our methodology combines the structural rigor of classical solvers with the adaptive capacity of AI to maintain precise control over both physical movement and evolving appearance. While we recognize the inherent complexity of full-scale multi-physics, evaluations against state-of-the-art baselines, such as Sora-2 and WonderPlay, demonstrate that PhasePlay achieves superior visual quality and physical fidelity across diverse interactive scenarios.