公开(公告)号：US20240249458A1

公开(公告)日：2024-07-25

申请号：US18364982

申请日：2023-08-03

Applicant: NVIDIA Corporation

Inventor： Chen Tessler ,  Gal Chechik ,  Yoni Kasten ,  Shie Mannor ,  Jason Peng

IPC: G06T13/40 ,  G06N3/08 ,  G06T13/80

CPC classification number: G06T13/40 ,  G06N3/08 ,  G06T13/80

Abstract: A conditional adversarial latent model (CALM) process can be used to generate reference motions from a set of original reference movements to create a library of new movements for an agent. The agent can be a virtual representation various types of characters, animals, or objects. The CALM process can receive a set of reference movements and a requested movement. An encoder can be used to map the requested movement onto a latent space. A low-level policy can be employed to produce a series of latent space joint movements for the agent. A conditional discriminator can be used to provide feedback to the low-level policy to produce stationary distributions over the states of the agent. A high-level policy can be employed to provide a macro movement control over the low-level policy movements, such as providing direction in the environment. The high-level policy can utilize a reward or a finite-state machine function.

公开(公告)号：US20240135618A1

公开(公告)日：2024-04-25

申请号：US18322319

申请日：2023-05-23

Applicant: NVIDIA Corporation

Inventor： Haotian Zhang ,  Ye Yuan ,  Jason Peng ,  Viktor Makoviichuk ,  Sanja Fidler

IPC: G06T13/40 ,  G06T7/70 ,  G06T17/00 ,  G06V40/20

CPC classification number: G06T13/40 ,  G06T7/70 ,  G06T17/00 ,  G06V40/23

Abstract: In various examples, artificial intelligence (AI) agents can be generated to synthesize more natural motion by simulated actors in various visualizations (such as video games or simulations). AI agents may employ one or more machine learning models and techniques, such as reinforcement learning, to enable synthesis of motion with enhanced realism. The AI agent can be trained based on widely-available broadcast video data, without the need for more costly and limited motion capture data. To account for the lower quality of such video data, various techniques can be employed, such as taking into account the motion of joints, and applying physics-based constraints on the actors, resulting in higher quality, more lifelike motion.

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.