公开(公告)号：US20210056408A1

公开(公告)日：2021-02-25

申请号：US16549072

申请日：2019-08-23

Applicant: Adobe Inc.

Inventor： Jonathan Brandt ,  Chen Fang ,  Byungmoon Kim ,  Biao Jia

IPC: G06N3/08 ,  G06N3/04 ,  G09G5/37

Abstract: The technology described herein is directed to a reinforcement learning based framework for training a natural media agent to learn a rendering policy without human supervision or labeled datasets. The reinforcement learning based framework feeds the natural media agent a training dataset to implicitly learn the rendering policy by exploring a canvas and minimizing a loss function. Once trained, the natural media agent can be applied to any reference image to generate a series (or sequence) of continuous-valued primitive graphic actions, e.g., sequence of painting strokes, that when rendered by a synthetic rendering environment on a canvas, reproduce an identical or transformed version of the reference image subject to limitations of an action space and the learned rendering policy.

公开(公告)号：US11113578B1

公开(公告)日：2021-09-07

申请号：US16847270

申请日：2020-04-13

Applicant: Adobe Inc.

Inventor： Jonathan Brandt ,  Radomir Mech ,  Ning Xu ,  Byungmoon Kim ,  Biao Jia

IPC: G06K9/62 ,  G06N3/08

Abstract: A non-photorealistic image rendering system and related techniques are described herein that train and implement machine learning models to reproduce digital images in accordance with various painting styles and constraints. The image rendering system can include a machine learning system that utilizes actor-critic based reinforcement learning techniques to train painting agents (e.g., models that include one or more neural networks) how to transform images into various artistic styles with minimal loss between the original images and the transformed images. The image rendering system can generate constrained painting agents, which correspond to painting agents that are further trained to reproduce images in accordance with one or more constraints. The constraints may include limitations of the color, width, size, and/or position of brushstrokes within reproduced images. These constrained painting agents may provide users with robust, flexible, and customizable non-photorealistic painting systems.

公开(公告)号：US11775817B2

公开(公告)日：2023-10-03

申请号：US16549072

申请日：2019-08-23

Applicant: Adobe Inc.

Inventor： Jonathan Brandt ,  Chen Fang ,  Byungmoon Kim ,  Biao Jia

IPC: G06N3/08 ,  G09G5/37 ,  G06N3/04

CPC classification number: G06N3/08 ,  G06N3/04 ,  G09G5/37

Abstract: Some embodiments involve a reinforcement learning based framework for training a natural media agent to learn a rendering policy without human supervision or labeled datasets. The reinforcement learning based framework feeds the natural media agent a training dataset to implicitly learn the rendering policy by exploring a canvas and minimizing a loss function. Once trained, the natural media agent can be applied to any reference image to generate a series (or sequence) of continuous-valued primitive graphic actions, e.g., sequence of painting strokes, that when rendered by a synthetic rendering environment on a canvas, reproduce an identical or transformed version of the reference image subject to limitations of an action space and the learned rendering policy.