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公开(公告)号：US11875435B2
公开(公告)日：2024-01-16
申请号：US17499611
申请日：2021-10-12
Applicant: Adobe Inc.
Inventor： Chinthala Pradyumna Reddy , Zhifei Zhang , Matthew Fisher , Hailin Jin , Zhaowen Wang , Niloy J Mitra
CPC classification number: G06T11/203 , G06T3/40
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media for accurately and flexibly generating scalable fonts utilizing multi-implicit neural font representations. For instance, the disclosed systems combine deep learning with differentiable rasterization to generate a multi-implicit neural font representation of a glyph. For example, the disclosed systems utilize an implicit differentiable font neural network to determine a font style code for an input glyph as well as distance values for locations of the glyph to be rendered based on a glyph label and the font style code. Further, the disclosed systems rasterize the distance values utilizing a differentiable rasterization model and combines the rasterized distance values to generate a permutation-invariant version of the glyph corresponding glyph set.
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公开(公告)号：US11551038B2
公开(公告)日：2023-01-10
申请号：US16459420
申请日：2019-07-01
Applicant: Adobe Inc.
Inventor： Siddhartha Chaudhuri , Vladimir Kim , Matthew Fisher , Sanjeev Muralikrishnan
Abstract: Techniques are described herein for generating and using a unified shape representation that encompasses features of different types of shape representations. In some embodiments, the unified shape representation is a unicode comprising a vector of embeddings and values for the embeddings. The embedding values are inferred, using a neural network that has been trained on different types of shape representations, based on a first representation of a three-dimensional (3D) shape. The first representation is received as input to the trained neural network and corresponds to a first type of shape representation. At least one embedding has a value dependent on a feature provided by a second type of shape representation and not provided by the first type of shape representation. The value of the at least one embedding is inferred based upon the first representation and in the absence of the second type of shape representation for the 3D shape.
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公开(公告)号：US11461377B2
公开(公告)日：2022-10-04
申请号：US17101891
申请日：2020-11-23
Applicant: Adobe Inc.
Inventor： Matthew Fisher
IPC: G06F16/34 , G06F40/284 , G06F40/289 , G06T19/20
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for generating a three-dimensional scene based on a natural language phrase. For example, the disclosed system can analyze a natural language phrase to determine dependencies involving entities and commands in the natural language phrase. The disclosed system can then use the dependencies to generate an entity-command representation of the natural language phrase. Additionally, the disclosed system can generate a semantic scene graph for the natural language phrase from the entity-command representation to indicate contextual relationships of the entities and commands. Furthermore, the disclosed system generates the requested three-dimensional scene by using at least one scene of a plurality of available three-dimensional scenes identified using the semantic scene graph of the natural language phrase.
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公开(公告)号：US20210264659A1
公开(公告)日：2021-08-26
申请号：US16799664
申请日：2020-02-24
Applicant: Adobe Inc.
Inventor： Vladimir Kim , Omid Poursaeed , Noam Aigerman , Matthew Fisher
Abstract: Certain embodiments involve techniques for generating a 3D representation based on a provided 2D image of an object. An image generation system receives the 2D image representation and generates a multi-dimensional vector of the input that represents the image. The image generation system samples a set of points and provides the set of points and the multi-dimensional vector to a neural network that was trained to predict a 3D surface representing the image such that the 3D surface is consistent with a 3D surface of the object calculated using an implicit function for representing the image. The neural network predicts, based on the multi-dimensional vector and the set of points, the 3D surface representing the object.
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35.发明申请公开(公告)号：US20210224465A1
公开(公告)日：2021-07-22
申请号：US16745210
申请日：2020-01-16
Applicant: Adobe Inc.
Inventor： Praveen Kumar Dhanuka , Arushi Jain , Matthew Fisher
IPC: G06F40/109 , G06F40/166 , G06T11/20
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that merge separate digital point text objects into a single merged digital text object while preserving the properties and original visual appearance associated with the digital text included therein. For example, the disclosed systems can determine point text character properties associated with the separate digital point text objects (e.g., rotations, baseline shifts, etc.). The disclosed systems can merge the separate digital point text objects into a single merged digital point text object and modify associated font character properties to reflect the determined point text character properties. Further, the disclosed systems can generate an area text object based on the merged digital point text object where the area text object includes the digital text and the font character properties.
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Patent Agency Ranking
公开(公告)号：US20210103607A1
公开(公告)日：2021-04-08
申请号：US17101891
申请日：2020-11-23
Applicant: Adobe Inc.
Inventor： Matthew Fisher
IPC: G06F16/34 , G06F40/284 , G06F40/289 , G06T19/20
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for generating a three-dimensional scene based on a natural language phrase. For example, the disclosed system can analyze a natural language phrase to determine dependencies involving entities and commands in the natural language phrase. The disclosed system can then use the dependencies to generate an entity-command representation of the natural language phrase. Additionally, the disclosed system can generate a semantic scene graph for the natural language phrase from the entity-command representation to indicate contextual relationships of the entities and commands. Furthermore, the disclosed system generates the requested three-dimensional scene by using at least one scene of a plurality of available three-dimensional scenes identified using the semantic scene graph of the natural language phrase.
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公开(公告)号：US20210004645A1
公开(公告)日：2021-01-07
申请号：US16459420
申请日：2019-07-01
Applicant: Adobe Inc.
Inventor： Siddhartha Chaudhuri , Vladimir Kim , Matthew Fisher , Sanjeev Muralikrishnan
Abstract: Techniques are described herein for generating and using a unified shape representation that encompasses features of different types of shape representations. In some embodiments, the unified shape representation is a unicode comprising a vector of embeddings and values for the embeddings. The embedding values are inferred, using a neural network that has been trained on different types of shape representations, based on a first representation of a three-dimensional (3D) shape. The first representation is received as input to the trained neural network and corresponds to a first type of shape representation. At least one embedding has a value dependent on a feature provided by a second type of shape representation and not provided by the first type of shape representation. The value of the at least one embedding is inferred based upon the first representation and in the absence of the second type of shape representation for the 3D shape.
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公开(公告)号：US20200372710A1
公开(公告)日：2020-11-26
申请号：US16985402
申请日：2020-08-05
Applicant: Adobe, Inc.
Inventor： Oliver Wang , Vladimir Kim , Matthew Fisher , Elya Shechtman , Chen-Hsuan Lin , Bryan Russell
Abstract: Techniques are disclosed for 3D object reconstruction using photometric mesh representations. A decoder is pretrained to transform points sampled from 2D patches of representative objects into 3D polygonal meshes. An image frame of the object is fed into an encoder to get an initial latent code vector. For each frame and camera pair from the sequence, a polygonal mesh is rendered at the given viewpoints. The mesh is optimized by creating a virtual viewpoint, rasterized to obtain a depth map. The 3D mesh projections are aligned by projecting the coordinates corresponding to the polygonal face vertices of the rasterized mesh to both selected viewpoints. The photometric error is determined from RGB pixel intensities sampled from both frames. Gradients from the photometric error are backpropagated into the vertices of the assigned polygonal indices by relating the barycentric coordinates of each image to update the latent code vector.
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公开(公告)号：US10511908B1
公开(公告)日：2019-12-17
申请号：US16298315
申请日：2019-03-11
Applicant: Adobe Inc.
Inventor： Matthew Fisher
IPC: H04B15/00 , H04R3/04 , G10L21/0232 , G06N20/00 , G06N3/08 , G10L21/0224
Abstract: Techniques are disclosed for reducing noise from an audio signal. A methodology implementing the techniques according to an embodiment includes generating a 2-dimensional (2D) spectrogram of a received audio signal and applying the 2D spectrogram to an image transformation neural network that is trained to transform the 2D spectrogram to generate an output spectrogram representing a denoised version of the received audio signal. The method further includes converting the output spectrogram to the time domain to generate the denoised audio signal. The neural network is trained on spectrogram images of clean and corrupted versions of training audio signals such that the trained neural network converts a spectrogram image of a corrupted audio signal into a spectrogram image more closely resembling a spectrogram of the associated clean audio signal. The denoising may also include removal of other degradation effects, including reverberation, unwanted voices, and unwanted music, from an audio signal.
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公开(公告)号：US11893338B2
公开(公告)日：2024-02-06
申请号：US17388744
申请日：2021-07-29
Applicant: Adobe Inc.
Inventor： Praveen Kumar Dhanuka , Arushi Jain , Matthew Fisher
IPC: G06F40/109 , G06T11/20 , G06F40/166
CPC classification number: G06F40/109 , G06F40/166 , G06T11/203
Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that merge separate digital point text objects into a single merged digital text object while preserving the properties and original visual appearance associated with the digital text included therein. For example, the disclosed systems can determine point text character properties associated with the separate digital point text objects (e.g., rotations, baseline shifts, etc.). The disclosed systems can merge the separate digital point text objects into a single merged digital point text object and modify associated font character properties to reflect the determined point text character properties. Further, the disclosed systems can generate an area text object based on the merged digital point text object where the area text object includes the digital text and the font character properties.
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