公开(公告)号：US20240251171A1

公开(公告)日：2024-07-25

申请号：US18406006

申请日：2024-01-05

Applicant: NVIDIA CORPORATION

Inventor： Iuri FROSIO ,  Yazhou XING ,  Chao LIU ,  Anjul PATNEY ,  Hongxu YIN ,  Amrita MAZUMDAR ,  Jan KAUTZ

IPC: H04N23/73 ,  H04N23/95

CPC classification number: H04N23/73 ,  H04N23/95

Abstract: One or more embodiments include receiving one or more frames of a live video captured by a video capturing device, wherein the one or more frames include a current frame that is most-recently captured, identifying a set of reference frames included in the one or more frames based on at least the current frame, wherein each frame in the set of reference frames has a different exposure level relative to the current frame, determining, using one or more neural networks, a set of missing details for one or more regions of the current frame based on the set of reference frames, generating an updated version of the current frame based on the set of details, and outputting the updated version of the current frame in real-time.

公开(公告)号：US20200226461A1

公开(公告)日：2020-07-16

申请号：US16248670

申请日：2019-01-15

Applicant: NVIDIA CORPORATION

Inventor： Greg HEINRICH ,  Iuri FROSIO

IPC: G06N3/08 ,  G06N3/04

Abstract: One embodiment of a method includes adjusting a plurality of hyperparameters corresponding to a plurality of neural networks trained asynchronously relative to each other using a plurality of computer systems. The method further includes asynchronously measuring one or more performance metrics associated with the plurality of neural networks being trained. The method further includes ceasing the adjusting of the plurality of hyperparameters corresponding to one or more of the plurality of neural networks if the one or more performance metrics associated with the one or more of the plurality of neural networks are below a threshold.

公开(公告)号：US20180075611A1

公开(公告)日：2018-03-15

申请号：US15823370

申请日：2017-11-27

Applicant: NVIDIA Corporation

Inventor： Gregory P. MEYER ,  Shalini GUPTA ,  Iuri FROSIO ,  Nagilla Dikpal REDDY ,  Jan KAUTZ

IPC: G06T7/507 ,  G06T7/70

CPC classification number: G06T7/507 ,  G06K9/6276 ,  G06T7/251 ,  G06T7/277 ,  G06T7/70 ,  G06T7/75 ,  G06T7/77 ,  G06T2200/28 ,  G06T2207/10016 ,  G06T2207/10028 ,  G06T2207/30201

Abstract: One embodiment of the present invention sets forth a technique for estimating a head pose of a user. The technique includes acquiring depth data associated with a head of the user and initializing each particle included in a set of particles with a different candidate head pose. The technique further includes performing one or more optimization passes that include performing at least one iterative closest point (ICP) iteration for each particle and performing at least one particle swarm optimization (PSO) iteration. Each ICP iteration includes rendering the three-dimensional reference model based on the candidate head pose associated with the particle and comparing the three-dimensional reference model to the depth data. Each PSO iteration comprises updating a global best head pose associated with the set of particles and modifying at least one candidate head pose. The technique further includes modifying a shape of the three-dimensional reference model based on depth data.

公开(公告)号：US20170046827A1

公开(公告)日：2017-02-16

申请号：US14825129

申请日：2015-08-12

Applicant: NVIDIA CORPORATION

Inventor： Gregory P. MEYER ,  Shalini GUPTA ,  Iuri FROSIO ,  Nagilla Dikpal REDDY ,  Jan KAUTZ

IPC: G06T7/00

CPC classification number: G06T7/507 ,  G06K9/6276 ,  G06T7/251 ,  G06T7/277 ,  G06T7/70 ,  G06T7/75 ,  G06T7/77 ,  G06T2200/28 ,  G06T2207/10016 ,  G06T2207/10028 ,  G06T2207/30201

Abstract: One embodiment of the present invention sets forth a technique for estimating a head pose of a user. The technique includes acquiring depth data associated with a head of the user and initializing each particle included in a set of particles with a different candidate head pose. The technique further includes performing one or more optimization passes that include performing at least one iterative closest point (ICP) iteration for each particle and performing at least one particle swarm optimization (PSO) iteration. Each ICP iteration includes rendering the three-dimensional reference model based on the candidate head pose associated with the particle and comparing the three-dimensional reference model to the depth data. Each PSO iteration comprises updating a global best head pose associated with the set of particles and modifying at least one candidate head pose. The technique further includes modifying a shape of the three-dimensional reference model based on depth data.

Abstract translation: 本发明的一个实施例提出了一种用于估计用户的头部姿势的技术。 该技术包括获取与用户头部相关联的深度数据并且初始化包含在具有不同候选头姿势的一组粒子中的每个粒子。 该技术还包括执行一个或多个优化遍，包括对每个粒子执行至少一个迭代最近点（ICP）迭代并且执行至少一个粒子群优化（PSO）迭代。 每个ICP迭代包括基于与粒子相关联的候选头部姿态来渲染三维参考模型，并将三维参考模型与深度数据进行比较。 每个PSO迭代包括更新与该组粒子相关联的全局最佳头部姿态并修改至少一个候选头姿势。 该技术还包括基于深度数据修改三维参考模型的形状。