AI RESEARCH
CausalGS: Learning Physical Causality of 3D Dynamic Scenes with Gaussian Representations
arXiv CS.CV
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ArXi:2605.10586v1 Announce Type: new Learning a physical model from video data that can comprehend physical laws and predict the future trajectories of objects is a formidable challenge in artificial intelligence. Prior approaches either leverage various Partial Differential Equations (PDEs) as soft constraints in the form of PINN losses, or integrate physics simulators into neural networks; however, they often rely on strong priors or high-quality geometry reconstruction.