🤖 AI Summary
Existing neural rendering approaches generally overlook the influence of temperature on visual appearance, making it difficult to faithfully simulate thermally coupled phenomena such as melting and solidification. This work introduces thermal phase-change dynamics into 3D Gaussian splatting for the first time by endowing Gaussian points with temperature attributes. It couples a numerical heat conduction–convection solver with Material Point Method (MPM) dynamics and proposes a topology-adaptive rendering strategy to mitigate visual artifacts caused by large deformations. The resulting framework achieves high-fidelity, physically consistent rendering of thermophysical dynamic scenes, demonstrating significantly improved realism and controllability over existing methods in complex phase-transition processes like melting and solidification.
📝 Abstract
Recent advances integrate physically grounded Newtonian dynamics with neural rendering frameworks, narrowing the gap between photorealistic scene reconstruction and physics-based animation. However, existing approaches focus on mechanically driven dynamics while neglecting temperature, a fundamental yet invisible physical factor underlying phenomena such as melting, solidification, and other thermomechanical processes. In this paper, we propose MeGAS, a novel framework that incorporates thermomechanical phase-change dynamics into 3D Gaussian Splatting (3DGS). Specifically, we propose a new thermomechanical dynamic Gaussian Splatting representation that augments 3DGS with temperature attributes and employs a heat advection-diffusion solver with MPM dynamics incorporating phase transitions, enabling physically plausible and visually realistic synthesis of thermophysical phenomena. Furthermore, a new topology-adaptive Gaussian rendering strategy is proposed to mitigate cracking and floaters under extreme deformation. Extensive experiments demonstrate that MeGAS produces physically consistent thermomechanical behavior while maintaining high-fidelity photorealistic rendering, advancing toward physics-integrated world models.