4DGS-WAM: Bridging Past and Future with an Object-Centric World Action Model based on 4D Gaussian Splatting

The paper introduces 4DGS-WAM, an object‑centric world action model that uses a 4D Gaussian Splatting representation to separate dynamic objects from a static background. By predicting future actions of dynamic actors and their Gaussian splat transformations, the model can reuse previously observed static content for future state generation, reducing redundant background processing. Experiments on the KITTI‑MOT dataset demonstrate the model’s ability to perform short‑horizon prediction and past reconstruction.

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