公开(公告)号：US20240185523A1

公开(公告)日：2024-06-06

申请号：US18339936

申请日：2023-06-22

Applicant: NVIDIA CORPORATION

Inventor： Dongsu ZHANG ,  Amlan KAR ,  Francis WILLIAMS ,  Zan GOJCIC ,  Karsten KREIS ,  Sanja FIDLER

IPC: G06T17/10

CPC classification number: G06T17/10

Abstract: In various examples, a technique for performing three-dimensional (3D) scene completion includes determining an initial representation of a first 3D scene. The technique also includes executing a machine learning model to generate a first update to the initial representation at a previous time step and a second update to the initial representation at a current time step, wherein the second update is generated based at least on a threshold applied to a set of predictions corresponding to the first update. The technique also includes generating a 3D model of the 3D scene based at least on the second update to the initial representation.

公开(公告)号：US20220405583A1

公开(公告)日：2022-12-22

申请号：US17681632

申请日：2022-02-25

Applicant: NVIDIA CORPORATION

Inventor： Arash VAHDAT ,  Karsten KREIS ,  Jan KAUTZ

IPC: G06N3/08

Abstract: One embodiment of the present invention sets forth a technique for training a generative model. The technique includes converting a first data point included in a training dataset into a first set of values associated with a base distribution for a score-based generative model. The technique also includes performing one or more denoising operations via the score-based generative model to convert the first set of values into a first set of latent variable values associated with a latent space. The technique further includes performing one or more additional operations to convert the first set of latent variable values into a second data point. Finally, the technique includes computing one or more losses based on the first data point and the second data point and generating a trained generative model based on the one or more losses, wherein the trained generative model includes the score-based generative model.

公开(公告)号：US20240161250A1

公开(公告)日：2024-05-16

申请号：US18485239

申请日：2023-10-11

Applicant: NVIDIA CORPORATION

Inventor： Yogesh BALAJI ,  Timo Oskari AILA ,  Miika AITTALA ,  Bryan CATANZARO ,  Xun HUANG ,  Tero Tapani KARRAS ,  Karsten KREIS ,  Samuli LAINE ,  Ming-Yu LIU ,  Seungjun NAH ,  Jiaming SONG ,  Arash VAHDAT ,  Qinsheng ZHANG

IPC: G06T5/00

CPC classification number: G06T5/002 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: Techniques are disclosed herein for generating a content item. The techniques include performing one or more first denoising operations based on an input and a first machine learning model to generate a first content item, and performing one or more second denoising operations based on the input, the first content item, and a second machine learning model to generate a second content item, where the first machine learning model is trained to denoise content items having an amount of corruption within a first corruption range, the second machine learning model is trained to denoise content items having an amount of corruption within a second corruption range, and the second corruption range is lower than the first corruption range.

公开(公告)号：US20220101145A1

公开(公告)日：2022-03-31

申请号：US17357738

申请日：2021-06-24

Applicant: NVIDIA CORPORATION

Inventor： Arash VAHDAT ,  Karsten KREIS ,  Zhisheng XIAO ,  Jan KAUTZ

IPC: G06N3/08 ,  G06N3/04

Abstract: One embodiment sets forth a technique for creating a generative model. The technique includes generating a trained generative model with a first component that converts data points in the training dataset into latent variable values, a second component that learns a distribution of the latent variable values, and a third component that converts the latent variable values into output distributions. The technique also includes training an energy-based model to learn an energy function based on values sampled from a first distribution associated with the training dataset and values sampled from a second distribution during operation of the trained generative model. The technique further includes creating a joint model that includes one or more portions of the trained generative model and the energy-based model, and that applies energy values from the energy-based model to samples from the second distribution to produce additional values used to generate a new data point.

公开(公告)号：US20220398697A1

公开(公告)日：2022-12-15

申请号：US17681625

申请日：2022-02-25

Applicant: NVIDIA CORPORATION

Inventor： Arash VAHDAT ,  Karsten KREIS ,  Jan KAUTZ

IPC: G06T5/00 ,  G06N3/08

Abstract: One embodiment of the present invention sets forth a technique for generating data. The technique includes sampling from a first distribution associated with the score-based generative model to generate a first set of values. The technique also includes performing one or more denoising operations via the score-based generative model to convert the first set of values into a first set of latent variable values associated with a latent space. The technique further includes converting the first set of latent variable values into a generative output.

公开(公告)号：US20220101122A1

公开(公告)日：2022-03-31

申请号：US17357728

申请日：2021-06-24

Applicant: NVIDIA CORPORATION

Inventor： Arash VAHDAT ,  Karsten KREIS ,  Zhisheng XIAO ,  Jan KAUTZ

IPC: G06N3/08 ,  G06N3/04 ,  G06K9/00

Abstract: One embodiment of the present invention sets forth a technique for generating data using a generative model. The technique includes sampling from one or more distributions of one or more variables to generate a first set of values for the one or more variables, where the one or more distributions are used during operation of one or more portions of the generative model. The technique also includes applying one or more energy values generated via an energy-based model to the first set of values to produce a second set of values for the one or more variables. The technique further includes either outputting the set of second values as output data or performing one or more operations based on the second set of values to generate output data.