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Google patents neural network to simulate object collisions in 3D environments

This filing describes a method for simulating how 3D objects interact over time using a graph neural network. It creates a map of connections between object surfaces that are close enough to collide, allowing the system to predict how the environment changes in the next moment.

Why it mattersThis technique could improve the realism and efficiency of physics simulations in video games, virtual reality, or robotics training environments.

In the filing’s own words

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for simulating a state of an environment over a sequence of time steps. In one aspect, a method comprises, at each of one or more time steps: obtaining an environment mesh representing the state of the environment at the time step; generating a graph representing the state of the environment at the time step, comprising: determining that a first face of a first object mesh is within a collision distance of a second face of a second object mesh; and in response, instantiating a face-face edge in the graph that connects: (i) a first set of graph nodes in the graph that represent the first face in the first object mesh, and (ii) a second set of graph nodes in the graph that represent the second face in the second object mesh.

What exactly they claim (claim 1)

1. A method performed by one or more computers for simulating a state of an environment over a sequence of time steps, the method comprising, at each of one or more time steps: obtaining an environment mesh representing the state of the environment at the time step, wherein environment mesh includes: (i) a first object mesh representing a first object in the environment, and (ii) a second object mesh representing a second object in the environment; generating a graph representing the state of the environment at the time step, wherein the graph comprises a plurality of graph nodes and a plurality of graph edges, and wherein generating the graph comprises: determining that a first face of the first object mesh is within a collision distance of a second face of the second object mesh; and in response, instantiating a face-face edge in the graph, wherein the face-face edge is a graph edge that connects: (i) a first set of graph nodes in the graph that represent the first face in the first object mesh, and (ii) a second set of graph nodes in the graph that represent the second face in the second object mesh; processing the graph using a graph neural network to generate an updated graph; and generating an updated environment mesh representing the state of the environment at a next time step using the updated graph.

Facts

Company
Google (DeepMind Technologies Limited)
Official title
GRAPH NEURAL NETWORKS THAT MODEL FACE-FACE INTERACTIONS BETWEEN MESHES
Publication number
US20260268042A1
Application number
18873202
Filed
Sep 27, 2023
Published
Sep 10, 2026
Inventors
7
Claims
29

Classified under

G06F 30/27 Physics & computingB25J 9/1671 Operations & transportG06N 3/084 Physics & computing
Read the full filing (official PDF) ↗ Open in the Patent Tracker

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Summaries are simplified from the filing’s own abstract and first claim. A patent filing is not a product announcement.
Patent data and drawings: U.S. Patent and Trademark Office (USPTO). Nebula is not affiliated with or endorsed by the USPTO.