公开(公告)号：US11875221B2

公开(公告)日：2024-01-16

申请号：US17468476

申请日：2021-09-07

Applicant: Adobe Inc.

Inventor： Wei-An Lin ,  Baldo Faieta ,  Cameron Smith ,  Elya Shechtman ,  Jingwan Lu ,  Jun-Yan Zhu ,  Niloy Mitra ,  Ratheesh Kalarot ,  Richard Zhang ,  Shabnam Ghadar ,  Zhixin Shu

IPC: G06N3/08 ,  G06F3/04845 ,  G06F3/04847 ,  G06T11/60 ,  G06T3/40 ,  G06N20/20 ,  G06T5/00 ,  G06T5/20 ,  G06T3/00 ,  G06T11/00 ,  G06F18/40 ,  G06F18/211 ,  G06F18/214 ,  G06F18/21 ,  G06N3/045

CPC classification number: G06N3/08 ,  G06F3/04845 ,  G06F3/04847 ,  G06F18/211 ,  G06F18/214 ,  G06F18/2163 ,  G06F18/40 ,  G06N3/045 ,  G06N20/20 ,  G06T3/0006 ,  G06T3/0093 ,  G06T3/40 ,  G06T3/4038 ,  G06T3/4046 ,  G06T5/005 ,  G06T5/20 ,  G06T11/001 ,  G06T11/60 ,  G06T2207/10024 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20221 ,  G06T2210/22

Abstract: Systems and methods generate a filtering function for editing an image with reduced attribute correlation. An image editing system groups training data into bins according to a distribution of a target attribute. For each bin, the system samples a subset of the training data based on a pre-determined target distribution of a set of additional attributes in the training data. The system identifies a direction in the sampled training data corresponding to the distribution of the target attribute to generate a filtering vector for modifying the target attribute in an input image, obtains a latent space representation of an input image, applies the filtering vector to the latent space representation of the input image to generate a filtered latent space representation of the input image, and provides the filtered latent space representation as input to a neural network to generate an output image with a modification to the target attribute.

公开(公告)号：US20230316590A1

公开(公告)日：2023-10-05

申请号：US17656907

申请日：2022-03-29

Applicant: Adobe Inc.

Inventor： Jaskirat Singh ,  Jose Ignacio Echevarria Vallespi ,  Cameron Smith

IPC: G06T11/00 ,  G06T7/194 ,  G06T7/70 ,  G06N3/08

CPC classification number: G06T11/00 ,  G06T7/194 ,  G06T7/70 ,  G06N3/08 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20021

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating painted digital images utilizing an intelligent painting process that includes progressive layering, sequential brushstroke guidance, and/or brushstroke regularization. For example, the disclosed systems utilize an image painting model to perform progressive layering to generate and apply digital brushstrokes in a progressive fashion for different layers associated with a background canvas and foreground objects. In addition, the disclosed systems utilize sequential brushstroke guidance to generate painted foreground objects by sequentially shifting through attention windows for regions of interest in a target digital image. Furthermore, the disclosed systems utilize brushstroke regularization to generate and apply an efficient brushstroke sequence to generate a painted digital image.

公开(公告)号：US20210264207A1

公开(公告)日：2021-08-26

申请号：US16802243

申请日：2020-02-26

Applicant: ADOBE INC.

Inventor： Cameron Smith ,  Yannick Hold-Geoffroy ,  Mariia Drozdova

IPC: G06K9/62 ,  G06K9/46 ,  G06K9/00

Abstract: Images can be edited to include features similar to a different target image. An unconditional generative adversarial network (GAN) is employed to edit features of an initial image based on a constraint determined from a target image. The constraint used by the GAN is determined from keypoints or segmentation masks of the target image, and edits are made to features of the initial image based on keypoints or segmentation masks of the initial image corresponding to those of the constraint from the target image. The GAN modifies the initial image based on a loss function having a variable for the constraint. The result of this optimization process is a modified initial image having features similar to the target image subject to the constraint determined from the identified keypoints or segmentation masks.