{
  "abstract": "A method for dynamic modeling and manipulation of multi-object scenes is described. The method includes using object-centric neural implicit scattering functions (OSFs) as object representations in a model-predictive control (MPC) framework for the multi-object scenes. The method also includes modeling a per-object light transport to enable compositional scene re-rendering under object rearrangement and varying lighting conditions. The method further includes applying inverse parameter estimation and graph neural network (GNN) dynamics models to estimate initial object poses and a light position in the multi-object scene. The method also includes manipulating an object perceived in the multi-object scene according to the applying of the inverse parameter estimation and the GNN dynamics models.",
  "assignees": [
    "TOYOTA JIDOSSHA KK",
    "TOYOTA RES INST INC",
    "UNIV LELAND STANFORD JUNIOR"
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    "CAI YANCHENG",
    "GAIDON ADRIEN DAVID",
    "LIU KATHERINE",
    "LI YUNZHU",
    "TIAN STEPHEN",
    "WU JIAJUN",
    "Yu hong-xing",
    "ZAKHAROV SERGEY"
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  "source_date": "2026-02-24",
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  "title": "Neural implicit scattering functions for inverse parameter estimation and dynamics modeling of multi-object interactions",
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