公开(公告)号：US20220067983A1

公开(公告)日：2022-03-03

申请号：US17006702

申请日：2020-08-28

Applicant: NVIDIA Corporation

Inventor： Sanja Fidler ,  David Acuna Marrero ,  Seung Wook Kim ,  Karsten Julian Kreis ,  Huan Ling

IPC: G06T9/00 ,  G06N3/08 ,  G06K9/62 ,  G06T7/11

Abstract: Apparatuses, systems, and techniques to generate complete depictions of objects based on a partial depiction of the object. In at least one embodiment, an image of a complete object is generated by one or more neural networks, based on an image of a portion of the object, using an encoder of the one or more neural networks trained using training data generated from output of a decoder of the one or more neural networks.

公开(公告)号：US12288277B2

公开(公告)日：2025-04-29

申请号：US17827394

申请日：2022-05-27

Applicant: NVIDIA Corporation

Inventor： Huan Ling ,  Karsten Kreis ,  Daiqing Li ,  Seung Wook Kim ,  Antonio Torralba Barriuso ,  Sanja Fidler

IPC: G06T7/10 ,  G06T11/60 ,  G06V10/774 ,  G06V10/776

Abstract: In various examples, high-precision semantic image editing for machine learning systems and applications are described. For example, a generative adversarial network (GAN) may be used to jointly model images and their semantic segmentations based on a same underlying latent code. Image editing may be achieved by using segmentation mask modifications (e.g., provided by a user, or otherwise) to optimize the latent code to be consistent with the updated segmentation, thus effectively changing the original, e.g., RGB image. To improve efficiency of the system, and to not require optimizations for each edit on each image, editing vectors may be learned in latent space that realize the edits, and that can be directly applied on other images with or without additional optimizations. As a result, a GAN in combination with the optimization approaches described herein may simultaneously allow for high precision editing in real-time with straightforward compositionality of multiple edits.

公开(公告)号：US20240171788A1

公开(公告)日：2024-05-23

申请号：US18181729

申请日：2023-03-10

Applicant: NVIDIA Corporation

Inventor： Karsten Julian Kreis ,  Robin Rombach ,  Andreas Blattmann ,  Seung Wook Kim ,  Huan Ling ,  Sanja Fidler ,  Tim Dockhorn

IPC: H04N21/2343 ,  G06T9/00 ,  G06V10/24 ,  G06V10/25 ,  G06V10/82 ,  H04N7/01

CPC classification number: H04N21/234363 ,  G06T9/00 ,  G06V10/24 ,  G06V10/25 ,  G06V10/82 ,  H04N7/0117

Abstract: In various examples, systems and methods are disclosed relating to aligning images into frames of a first video using at least one first temporal attention layer of a neural network model. The first video has a first spatial resolution. A second video having a second spatial resolution is generated by up-sampling the first video using at least one second temporal attention layer of an up-sampler neural network model, wherein the second spatial resolution is higher than the first spatial resolution.

公开(公告)号：US11494976B2

公开(公告)日：2022-11-08

申请号：US17193405

申请日：2021-03-05

Applicant: Nvidia Corporation

Inventor： Wenzheng Chen ,  Yuxuan Zhang ,  Sanja Fidler ,  Huan Ling ,  Jun Gao ,  Antonio Torralba Barriuso

IPC: G06T17/00 ,  G06N3/04 ,  G06N3/08 ,  G06T15/10

Abstract: Approaches are presented for training an inverse graphics network. An image synthesis network can generate training data for an inverse graphics network. In turn, the inverse graphics network can teach the synthesis network about the physical three-dimensional (3D) controls. Such an approach can provide for accurate 3D reconstruction of objects from 2D images using the trained inverse graphics network, while requiring little annotation of the provided training data. Such an approach can extract and disentangle 3D knowledge learned by generative models by utilizing differentiable renderers, enabling a disentangled generative model to function as a controllable 3D “neural renderer,” complementing traditional graphics renderers.