公开(公告)号：US20240161355A1

公开(公告)日：2024-05-16

申请号：US18419287

申请日：2024-01-22

Applicant: Adobe Inc. ,  University of Massachusetts

Inventor： Aaron Hertzmann ,  Matthew Fisher ,  Difan Liu ,  Evangelos Kalogerakis

IPC: G06T11/00 ,  G06N3/045 ,  G06T11/20

CPC classification number: G06T11/001 ,  G06N3/045 ,  G06T11/203 ,  G06T2200/04

Abstract: Techniques for generating a stylized drawing of three-dimensional (3D) shapes using neural networks are disclosed. A processing device generates a set of vector curve paths from a viewpoint of a 3D shape; extracts, using a first neural network of a plurality of neural networks of a machine learning model, surface geometry features of the 3D shape based on geometric properties of surface points of the 3D shape; determines, using a second neural network of the plurality of neural networks of the machine learning model, a set of at least one predicted stroke attribute based on the surface geometry features and a predetermined drawing style; generates, based on the at least one predicted stroke attribute, a set of vector stroke paths corresponding to the set of vector curve paths; and outputs a two-dimensional (2D) stylized stroke drawing of the 3D shape based at least on the set of vector stroke paths.

公开(公告)号：US20240338869A1

公开(公告)日：2024-10-10

申请号：US18474536

申请日：2023-09-26

Applicant: ADOBE INC.

Inventor： Yuqian Zhou ,  Krishna Kumar Singh ,  Zhifei Zhang ,  Difan Liu ,  Zhe Lin ,  Jianming Zhang ,  Qing Liu ,  Jingwan Lu ,  Elya Shechtman ,  Sohrab Amirghodsi ,  Connelly Stuart Barnes

IPC: G06T11/60

CPC classification number: G06T11/60

Abstract: An image processing system obtains an input image (e.g., a user provided image, etc.) and a mask indicating an edit region of the image. A user selects an image editing mode for an image generation network from a plurality of image editing modes. The image generation network generates an output image using the input image, the mask, and the image editing mode.

公开(公告)号：US11880913B2

公开(公告)日：2024-01-23

申请号：US17452568

申请日：2021-10-27

Applicant: Adobe Inc. ,  University of Massachusetts

Inventor： Aaron Hertzmann ,  Matthew Fisher ,  Difan Liu ,  Evangelos Kalogerakis

IPC: G06T11/00 ,  G06T11/20 ,  G06N3/045

CPC classification number: G06T11/001 ,  G06N3/045 ,  G06T11/203 ,  G06T2200/04

Abstract: Techniques for generating a stylized drawing of three-dimensional (3D) shapes using neural networks are disclosed. A processing device generates a set of vector curve paths from a viewpoint of a 3D shape; extracts, using a first neural network of a plurality of neural networks of a machine learning model, surface geometry features of the 3D shape based on geometric properties of surface points of the 3D shape; determines, using a second neural network of the plurality of neural networks of the machine learning model, a set of at least one predicted stroke attribute based on the surface geometry features and a predetermined drawing style; generates, based on the at least one predicted stroke attribute, a set of vector stroke paths corresponding to the set of vector curve paths; and outputs a two-dimensional (2D) stylized stroke drawing of the 3D shape based at least on the set of vector stroke paths.

公开(公告)号：US20250061548A1

公开(公告)日：2025-02-20

申请号：US18452150

申请日：2023-08-18

Applicant: ADOBE INC.

Inventor： Difan Liu ,  Siddharth Iyer ,  Ryan Joe Murdock

IPC: G06T5/00 ,  G06T5/50

Abstract: Systems and methods for generating images using hybrid sampling include obtaining a noisy image and generating a first denoised image during a first reverse diffusion phase using a diffusion neural network. The first denoised image is generated based on a first sampler that uses a first sampling density during at least a portion of the first reverse diffusion phase. Subsequently, a second denoised image is generated based on the first denoised image during a second reverse diffusion phase using the diffusion neural network. The second denoised image is generated based on a second sampler that uses a second sampling density different from the first sampling density during at least a portion of the second reverse diffusion phase.

公开(公告)号：US20230360376A1

公开(公告)日：2023-11-09

申请号：US17744995

申请日：2022-05-16

Applicant: Adobe Inc.

Inventor： Tobias Hinz ,  Taesung Park ,  Richard Zhang ,  Matthew David Fisher ,  Difan Liu ,  Evangelos Kalogerakis

IPC: G06V10/774 ,  G06V10/22 ,  G06T3/40

CPC classification number: G06V10/7753 ,  G06V10/235 ,  G06T3/4046

Abstract: Semantic fill techniques are described that support generating fill and editing images from semantic inputs. A user input, for example, is received by a semantic fill system that indicates a selection of a first region of a digital image and a corresponding semantic label. The user input is utilized by the semantic fill system to generate a guidance attention map of the digital image. The semantic fill system leverages the guidance attention map to generate a sparse attention map of a second region of the digital image. A semantic fill of pixels is generated for the first region based on the semantic label and the sparse attention map. The edited digital image is displayed in a user interface.

公开(公告)号：US20230109732A1

公开(公告)日：2023-04-13

申请号：US17452568

申请日：2021-10-27

Applicant: Adobe Inc. ,  University of Massachusetts

Inventor： Aaron Hertzmann ,  Matthew Fisher ,  Difan Liu ,  Evangelos Kalogerakis

IPC: G06T11/00 ,  G06T11/20 ,  G06N3/04

Abstract: Techniques for generating a stylized drawing of three-dimensional (3D) shapes using neural networks are disclosed. A processing device generates a set of vector curve paths from a viewpoint of a 3D shape; extracts, using a first neural network of a plurality of neural networks of a machine learning model, surface geometry features of the 3D shape based on geometric properties of surface points of the 3D shape; determines, using a second neural network of the plurality of neural networks of the machine learning model, a set of at least one predicted stroke attribute based on the surface geometry features and a predetermined drawing style; generates, based on the at least one predicted stroke attribute, a set of vector stroke paths corresponding to the set of vector curve paths; and outputs a two-dimensional (2D) stylized stroke drawing of the 3D shape based at least on the set of vector stroke paths.