公开(公告)号：US20240161377A1

公开(公告)日：2024-05-16

申请号：US18194116

申请日：2023-03-31

Applicant: NVIDIA Corporation

Inventor： Zhengyi Luo ,  Jason Peng ,  Sanja Fidler ,  Or Litany ,  Davis Winston Rempe ,  Ye Yuan

IPC: G06T13/40 ,  G06N3/006

CPC classification number: G06T13/40 ,  G06N3/006

Abstract: In various examples, systems and methods are disclosed relating to generating a simulated environment and update a machine learning model to move each of a plurality of human characters having a plurality of body shapes, to follow a corresponding trajectory within the simulated environment as conditioned on a respective body shape. The simulated human characters can have diverse characteristics (such as gender, body proportions, body shape, and so on) as observed in real-life crowds. A machine learning model can determine an action for a human character in a simulated environment, based at least on a humanoid state, a body shape, and task-related features. The task-related features can include an environmental feature and a trajectory.

公开(公告)号：US20240160888A1

公开(公告)日：2024-05-16

申请号：US18193982

申请日：2023-03-31

Applicant: NVIDIA Corporation

Inventor： Davis Winston Rempe ,  Karsten Julian Kreis ,  Sanja Fidler ,  Or Litany ,  Jonah Philion

IPC: G06N3/02

CPC classification number: G06N3/02

Abstract: In various examples, systems and methods are disclosed relating to neural networks for realistic and controllable agent simulation using guided trajectories. The neural networks can be configured using training data including trajectories and other state data associated with subjects or agents and remote or neighboring subjects or agents, as well as context data representative of an environment in which the subjects are present. The trajectories can be determining using the neural networks and using various forms of guidance for controllability, such as for waypoint navigation, obstacle avoidance, and group movement.