  This paper studies the problem of estimating physical properties (system
identification) through visual observations. To facilitate geometry-aware
guidance in physical property estimation, we introduce a novel hybrid framework
that leverages 3D Gaussian representation to not only capture explicit shapes
but also enable the simulated continuum to deduce implicit shapes during
training. We propose a new dynamic 3D Gaussian framework based on motion
factorization to recover the object as 3D Gaussian point sets across different
time states. Furthermore, we develop a coarse-to-fine filling strategy to
generate the density fields of the object from the Gaussian reconstruction,
allowing for the extraction of object continuums along with their surfaces and
the integration of Gaussian attributes into these continuums. In addition to
the extracted object surfaces, the Gaussian-informed continuum also enables the
rendering of object masks during simulations, serving as implicit shape
guidance for physical property estimation. Extensive experimental evaluations
demonstrate that our pipeline achieves state-of-the-art performance across
multiple benchmarks and metrics. Additionally, we illustrate the effectiveness
of the proposed method through real-world demonstrations, showcasing its
practical utility. Our project page is at https://jukgei.github.io/project/gic.
