Researchers have developed a framework called GCA (Gaussian Constitutive Alignment) that can learn implicit constitutive laws from videos of deformable objects. This approach uses a single fixed-viewpoint video and a static multi-view scan to infer the object's physical dynamics, without relying on predefined equations or pixel-level color supervision. The method combines LoRA-based adaptation with two alignment modules: Rank-based Depth-Geometric Anchors (RDGA) for robust geometric constraints and Constitutive Prior Regularizer (CPR) for integrating classical constitutive models as soft differentiable priors. --- Why it matters: This research matters because it enables the development of more accurate and physically interpretable methods for learning implicit constitutive laws from monocular videos. This could have significant implications for applications such as robotics, computer vision, and materials science. Source: https://arxiv.org/abs/2608.22102