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公开(公告)号：US11605001B2
公开(公告)日：2023-03-14
申请号：US17160585
申请日：2021-01-28
Applicant: NVIDIA Corporation
Inventor： Tero Tapani Karras , Samuli Matias Laine , Jaakko T. Lehtinen , Miika Samuli Aittala , Janne Johannes Hellsten , Timo Oskari Aila
Abstract: A style-based generative network architecture enables scale-specific control of synthesized output data, such as images. During training, the style-based generative neural network (generator neural network) includes a mapping network and a synthesis network. During prediction, the mapping network may be omitted, replicated, or evaluated several times. The synthesis network may be used to generate highly varied, high-quality output data with a wide variety of attributes. For example, when used to generate images of people's faces, the attributes that may vary are age, ethnicity, camera viewpoint, pose, face shape, eyeglasses, colors (eyes, hair, etc.), hair style, lighting, background, etc. Depending on the task, generated output data may include images, audio, video, three-dimensional (3D) objects, text, etc.
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公开(公告)号：US20220405880A1
公开(公告)日：2022-12-22
申请号：US17562494
申请日：2021-12-27
Applicant: NVIDIA Corporation
Inventor： Tero Tapani Karras , Miika Samuli Aittala , Samuli Matias Laine , Erik Andreas Härkönen , Janne Johannes Hellsten , Jaakko T. Lehtinen , Timo Oskari Aila
Abstract: Systems and methods are disclosed that improve output quality of any neural network, particularly an image generative neural network. In the real world, details of different scale tend to transform hierarchically. For example, moving a person's head causes the nose to move, which in turn moves the skin pores on the nose. Conventional generative neural networks do not synthesize images in a natural hierarchical manner: the coarse features seem to mainly control the presence of finer features, but not the precise positions of the finer features. Instead, much of the fine detail appears to be fixed to pixel coordinates which is a manifestation of aliasing. Aliasing breaks the illusion of a solid and coherent object moving in space. A generative neural network with reduced aliasing provides an architecture that exhibits a more natural transformation hierarchy, where the exact sub-pixel position of each feature is inherited from underlying coarse features.
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Patent Agency Ranking
公开(公告)号：US20220189100A1
公开(公告)日：2022-06-16
申请号：US17365574
申请日：2021-07-01
Applicant: NVIDIA Corporation
Inventor： Onni August Kosomaa , Jaakko T. Lehtinen , Samuli Matias Laine , Tero Tapani Karras , Miika Samuli Aittala
Abstract: A three-dimensional (3D) density volume of an object is constructed from tomography images (e.g., x-ray images) of the object. The tomography images are projection images that capture all structures of an object (e.g., human body) between a beam source and imaging sensor. The beam effectively integrates along a path through the object producing a tomography image at the imaging sensor, where each pixel represents attenuation. A 3D reconstruction pipeline includes a first neural network model, a fixed function backprojection unit, and a second neural network model. Given information for the capture environment, the tomography images are processed by the reconstruction pipeline to produce a reconstructed 3D density volume of the object. In contrast with a set of 2D slices, the entire 3D density volume is reconstructed, so two-dimensional (2D) density images may be produced by slicing through any portion of the 3D density volume at any angle.
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公开(公告)号：US20220189011A1
公开(公告)日：2022-06-16
申请号：US17365645
申请日：2021-07-01
Applicant: NVIDIA Corporation
Inventor： Onni August Kosomaa , Jaakko T. Lehtinen , Samuli Matias Laine , Tero Tapani Karras , Miika Samuli Aittala
Abstract: A three-dimensional (3D) density volume of an object is constructed from tomography images (e.g., x-ray images) of the object. The tomography images are projection images that capture all structures of an object (e.g., human body) between a beam source and imaging sensor. The beam effectively integrates along a path through the object producing a tomography image at the imaging sensor, where each pixel represents attenuation. A 3D reconstruction pipeline includes a first neural network model, a fixed function backprojection unit, and a second neural network model. Given information for the capture environment, the tomography images are processed by the reconstruction pipeline to produce a reconstructed 3D density volume of the object. In contrast with a set of 2D slices, the entire 3D density volume is reconstructed, so two-dimensional (2D) density images may be produced by slicing through any portion of the 3D density volume at any angle.
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公开(公告)号：US20210150357A1
公开(公告)日：2021-05-20
申请号：US17160648
申请日：2021-01-28
Applicant: NVIDIA Corporation
Inventor： Tero Tapani Karras , Samuli Matias Laine , Jaakko T. Lehtinen , Miika Samuli Aittala , Janne Johannes Hellsten , Timo Oskari Aila
Abstract: A style-based generative network architecture enables scale-specific control of synthesized output data, such as images. During training, the style-based generative neural network (generator neural network) includes a mapping network and a synthesis network. During prediction, the mapping network may be omitted, replicated, or evaluated several times. The synthesis network may be used to generate highly varied, high-quality output data with a wide variety of attributes. For example, when used to generate images of people's faces, the attributes that may vary are age, ethnicity, camera viewpoint, pose, face shape, eyeglasses, colors (eyes, hair, etc.), hair style, lighting, background, etc. Depending on the task, generated output data may include images, audio, video, three-dimensional (3D) objects, text, etc.
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公开(公告)号：US20210150354A1
公开(公告)日：2021-05-20
申请号：US17143608
申请日：2021-01-07
Applicant: NVIDIA Corporation
Inventor： Tero Tapani Karras , Samuli Matias Laine , David Patrick Luebke , Jaakko T. Lehtinen , Miika Samuli Aittala , Timo Oskari Aila , Ming-Yu Liu , Arun Mohanray Mallya , Ting-Chun Wang
Abstract: A latent code defined in an input space is processed by the mapping neural network to produce an intermediate latent code defined in an intermediate latent space. The intermediate latent code may be used as appearance vector that is processed by the synthesis neural network to generate an image. The appearance vector is a compressed encoding of data, such as video frames including a person's face, audio, and other data. Captured images may be converted into appearance vectors at a local device and transmitted to a remote device using much less bandwidth compared with transmitting the captured images. A synthesis neural network at the remote device reconstructs the images for display.
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公开(公告)号：US20210042503A1
公开(公告)日：2021-02-11
申请号：US17069478
申请日：2020-10-13
Applicant: NVIDIA Corporation
Inventor： Tero Tapani Karras , Samuli Matias Laine , David Patrick Luebke , Jaakko T. Lehtinen , Miika Samuli Aittala , Timo Oskari Aila , Ming-Yu Liu , Arun Mohanray Mallya , Ting-Chun Wang
Abstract: A latent code defined in an input space is processed by the mapping neural network to produce an intermediate latent code defined in an intermediate latent space. The intermediate latent code may be used as appearance vector that is processed by the synthesis neural network to generate an image. The appearance vector is a compressed encoding of data, such as video frames including a person's face, audio, and other data. Captured images may be converted into appearance vectors at a local device and transmitted to a remote device using much less bandwidth compared with transmitting the captured images. A synthesis neural network at the remote device reconstructs the images for display.
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