公开(公告)号：US20210110589A1

公开(公告)日：2021-04-15

申请号：US17083899

申请日：2020-10-29

Applicant: Adobe Inc.

Inventor： Jianming Zhang ,  Zhe Lin ,  Radomir Mech ,  Xiaohui Shen

IPC: G06T11/60 ,  G06T3/00 ,  G06T7/12

Abstract: Embodiments of the present invention are directed to facilitating region of interest preservation. In accordance with some embodiments of the present invention, a region of interest preservation score using adaptive margins is determined. The region of interest preservation score indicates an extent to which at least one region of interest is preserved in a candidate image crop associated with an image. A region of interest positioning score is determined that indicates an extent to which a position of the at least one region of interest is preserved in the candidate image crop associated with the image. The region of interest preservation score and/or the preserving score are used to select a set of one or more candidate image crops as image crop suggestions.

公开(公告)号：US20200065956A1

公开(公告)日：2020-02-27

申请号：US16670314

申请日：2019-10-31

Applicant: Adobe Inc.

Inventor： Xiaohui Shen ,  Zhe Lin ,  Shu Kong ,  Radomir Mech

IPC: G06T7/00 ,  G06K9/62 ,  G06N3/08 ,  G06N3/04

Abstract: Systems and methods are disclosed for estimating aesthetic quality of digital images using deep learning. In particular, the disclosed systems and methods describe training a neural network to generate an aesthetic quality score digital images. In particular, the neural network includes a training structure that compares relative rankings of pairs of training images to accurately predict a relative ranking of a digital image. Additionally, in training the neural network, an image rating system can utilize content-aware and user-aware sampling techniques to identify pairs of training images that have similar content and/or that have been rated by the same or different users. Using content-aware and user-aware sampling techniques, the neural network can be trained to accurately predict aesthetic quality ratings that reflect subjective opinions of most users as well as provide aesthetic scores for digital images that represent the wide spectrum of aesthetic preferences of various users.

公开(公告)号：US10552730B2

公开(公告)日：2020-02-04

申请号：US14788178

申请日：2015-06-30

Applicant: ADOBE INC.

Inventor： Mehmet Ersin Yumer ,  Radomir Mech ,  Paul John Asente ,  Gavin Stuart Peter Miller

IPC: G06N3/04

Abstract: An intuitive object-generation experience is provided by employing an autoencoder neural network to reduce the dimensionality of a procedural model. A set of sample objects are generated using the procedural model. In embodiments, the sample objects may be selected according to visual features such that the sample objects are uniformly distributed in visual appearance. Both procedural model parameters and visual features from the sample objects are used to train an autoencoder neural network, which maps a small number of new parameters to the larger number of procedural model parameters of the original procedural model. A user interface may be provided that allows users to generate new objects by adjusting the new parameters of the trained autoencoder neural network, which outputs procedural model parameters. The output procedural model parameters may be provided to the procedural model to generate the new objects.

公开(公告)号：US20190362199A1

公开(公告)日：2019-11-28

申请号：US15989436

申请日：2018-05-25

Applicant: Adobe Inc.

Inventor： Zhe Lin ,  Xiaohui Shen ,  Shanghang Zhang ,  Radomir Mech

IPC: G06K9/62 ,  G06K9/46 ,  G06T7/00 ,  G06N3/08

Abstract: Techniques are disclosed for blur classification. The techniques utilize an image content feature map, a blur map, and an attention map, thereby combining low-level blur estimation with a high-level understanding of important image content in order to perform blur classification. The techniques allow for programmatically determining if blur exists in an image, and determining what type of blur it is (e.g., high blur, low blur, middle or neutral blur, or no blur). According to one example embodiment, if blur is detected, an estimate of spatially-varying blur amounts is performed and blur desirability is categorized in terms of image quality.

公开(公告)号：US20240161366A1

公开(公告)日：2024-05-16

申请号：US18055584

申请日：2022-11-15

Applicant: Adobe Inc.

Inventor： Radomir Mech ,  Nathan Carr ,  Matheus Gadelha

IPC: G06T11/60 ,  G06T7/70 ,  G06T17/20 ,  G06T19/20

CPC classification number: G06T11/60 ,  G06T7/70 ,  G06T17/20 ,  G06T19/20 ,  G06T2219/2004

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for generating three-dimensional meshes representing two-dimensional images for editing the two-dimensional images. The disclosed system utilizes a first neural network to determine density values of pixels of a two-dimensional image based on estimated disparity. The disclosed system samples points in the two-dimensional image according to the density values and generates a tessellation based on the sampled points. The disclosed system utilizes a second neural network to estimate camera parameters and modify the three-dimensional mesh based on the estimated camera parameters of the pixels of the two-dimensional image. In one or more additional embodiments, the disclosed system generates a three-dimensional mesh to modify a two-dimensional image according to a displacement input. Specifically, the disclosed system maps the three-dimensional mesh to the two-dimensional image, modifies the three-dimensional mesh in response to a displacement input, and updates the two-dimensional image.

公开(公告)号：US20230360310A1

公开(公告)日：2023-11-09

申请号：US17662287

申请日：2022-05-06

Applicant: ADOBE INC.

Inventor： Paul Augusto Guerrero ,  Milos Hasan ,  Kalyan K. Sunkavalli ,  Radomir Mech ,  Tamy Boubekeur ,  Niloy Jyoti Mitra

IPC: G06T15/04 ,  G06T17/00 ,  G06V10/44 ,  G06V10/426 ,  G06V10/774 ,  G06V10/776

CPC classification number: G06T15/04 ,  G06T17/00 ,  G06V10/44 ,  G06V10/426 ,  G06V10/7747 ,  G06V10/776

Abstract: Aspects of a system and method for procedural media generation include generating a sequence of operator types using a node generation network; generating a sequence of operator parameters for each operator type of the sequence of operator types using a parameter generation network; generating a sequence of directed edges based on the sequence of operator types using an edge generation network; combining the sequence of operator types, the sequence of operator parameters, and the sequence of directed edges to obtain a procedural media generator, wherein each node of the procedural media generator comprises an operator that includes an operator type from the sequence of operator types, a corresponding sequence of operator parameters, and an input connection or an output connection from the sequence of directed edges that connects the node to another node of the procedural media generator; and generating a media asset using the procedural media generator.

公开(公告)号：US11769279B2

公开(公告)日：2023-09-26

申请号：US17317246

申请日：2021-05-11

Applicant: Adobe Inc.

Inventor： Giorgio Gori ,  Tamy Boubekeur ,  Radomir Mech ,  Nathan Aaron Carr ,  Matheus Abrantes Gadelha ,  Duygu Ceylan Aksit

IPC: G06T11/20 ,  G06N20/00 ,  G06T9/00 ,  G06N7/00 ,  G06N7/01

CPC classification number: G06T11/203 ,  G06N7/01 ,  G06N20/00 ,  G06T9/00 ,  G06T2200/24

Abstract: Generative shape creation and editing is leveraged in a digital medium environment. An object editor system represents a set of training shapes as sets of visual elements known as “handles,” and converts sets of handles into signed distance field (SDF) representations. A handle processor model is then trained using the SDF representations to enable the handle processor model to generate new shapes that reflect salient visual features of the training shapes. The trained handle processor model, for instance, generates new sets of handles based on salient visual features learned from the training handle set. Thus, utilizing the described techniques, accurate characterizations of a set of shapes can be learned and used to generate new shapes. Further, generated shapes can be edited and transformed in different ways.

公开(公告)号：US20230147722A1

公开(公告)日：2023-05-11

申请号：US17520071

申请日：2021-11-05

Applicant: Adobe Inc.

Inventor： Marissa Ramirez de Chanlatte ,  Radomir Mech ,  Matheus Gadelha ,  Thibault Groueix

IPC: G06T19/20 ,  G06K9/62 ,  G06T7/50 ,  G06N3/04

CPC classification number: G06T19/20 ,  G06K9/6256 ,  G06T7/50 ,  G06N3/04 ,  G06T2219/2004

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that tune a 3D-object-reconstruction-machine-learning model to reconstruct 3D models of objects from real images using real images as training data. For instance, the disclosed systems can determine a depth map for a real two-dimensional (2D) image and then reconstruct a 3D model of a digital object in the real 2D image based on the depth map. By using a depth map for a real 2D image, the disclosed systems can generate reconstructed 3D models that better conform to the shape of digital objects in real images than existing systems and use such reconstructed 3D models to generate more realistic looking visual effects (e.g., shadows, relighting).

公开(公告)号：US11244502B2

公开(公告)日：2022-02-08

申请号：US15825959

申请日：2017-11-29

Applicant: Adobe Inc.

Inventor： Vojt{hacek over (e)}ch Krs ,  Radomir Mech ,  Nathan A. Carr

IPC: G06T17/20 ,  G06T17/10

Abstract: Techniques are disclosed for generation of 3D structures. A methodology implementing the techniques according to an embodiment includes initializing systems configured to provide rules that specify edge connections between vertices and parametric properties of the vertices. The rules are applied to an initial set of vertices to generate 3D graphs for each of these vertex-rule-graph (VRG) systems. The initial set of vertices is associated with provided interaction surfaces of a 3D model. Skeleton geometries are generated for the 3D graphs, and an associated objective function is calculated. The objective function is configured to evaluate the fitness of the skeleton geometries based on given geometric and functional constraints. A 3D structure is generated through an iterative application of genetic programming techniques applied to the VRG systems to minimize the objective function. Receiving updated constraints and interaction surfaces, for incorporation in the iterative process.

公开(公告)号：US11189060B2

公开(公告)日：2021-11-30

申请号：US16863540

申请日：2020-04-30

Applicant: Adobe Inc.

Inventor： Milos Hasan ,  Liang Shi ,  Tamy Boubekeur ,  Kalyan Sunkavalli ,  Radomir Mech

IPC: G06T11/00 ,  G06T11/40 ,  G06T15/04 ,  G06N3/08

Abstract: The present disclosure relates to using end-to-end differentiable pipeline for optimizing parameters of a base procedural material to generate a procedural material corresponding to a target physical material. For example, the disclosed systems can receive a digital image of a target physical material. In response, the disclosed systems can retrieve a differentiable procedural material for use as a base procedural material in response. The disclosed systems can compare a digital image of the base procedural material with the digital image of the target physical material using a loss function, such as a style loss function that compares visual appearance. Based on the determined loss, the disclosed systems can modify the parameters of the base procedural material to determine procedural material parameters for the target physical material. The disclosed systems can generate a procedural material corresponding to the base procedural material using the determined procedural material parameters.