公开(公告)号：US12271804B2

公开(公告)日：2025-04-08

申请号：US17870496

申请日：2022-07-21

Applicant: Adobe Inc.

Inventor： Mang Tik Chiu ,  Connelly Barnes ,  Zijun Wei ,  Zhe Lin ,  Yuqian Zhou ,  Xuaner Zhang ,  Sohrab Amirghodsi ,  Florian Kainz ,  Elya Shechtman

IPC: G06N3/045 ,  G06T3/40 ,  G06T5/30 ,  G06T5/77 ,  G06T7/11 ,  G06T7/143 ,  G06T7/187 ,  G06T7/62

Abstract: Embodiments are disclosed for performing wire segmentation of images using machine learning. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an input image, generating, by a first trained neural network model, a global probability map representation of the input image indicating a probability value of each pixel including a representation of wires, and identifying regions of the input image indicated as including the representation of wires. The disclosed systems and methods further comprise, for each region from the identified regions, concatenating the region and information from the global probability map to create a concatenated input, and generating, by a second trained neural network model, a local probability map representation of the region based on the concatenated input, indicating pixels of the region including representations of wires. The disclosed systems and methods further comprise aggregating local probability maps for each region.

公开(公告)号：US20240404188A1

公开(公告)日：2024-12-05

申请号：US18205279

申请日：2023-06-02

Applicant: Adobe Inc.

Inventor： He Zhang ,  Zijun Wei ,  Zhixin Shu ,  Yiqun Mei ,  Yilin Wang ,  Xuaner Zhang ,  Shi Yan ,  Jianming Zhang

IPC: G06T15/50 ,  G06T7/11 ,  G06T15/04 ,  G06T15/60 ,  G06T15/80

Abstract: In accordance with the described techniques, a portrait relighting system receives user input defining one or more markings drawn on a portrait image. Using one or more machine learning models, the portrait relighting system generates an albedo representation of the portrait image by removing lighting effects from the portrait image. Further, the portrait relighting system generates a shading map of the portrait image using the one or more machine learning models by designating the one or more markings as a lighting condition, and applying the lighting condition to a geometric representation of the portrait image. The one or more machine learning models are further employed to generate a relit portrait image based on the albedo representation and the shading map.

公开(公告)号：US20240202989A1

公开(公告)日：2024-06-20

申请号：US18067878

申请日：2022-12-19

Applicant: Adobe Inc.

Inventor： Ethan Tseng ,  Zhihao Xia ,  Yifei Fan ,  Xuaner Zhang ,  Peter Merrill ,  Lars Jebe ,  Jiawen Chen

IPC: G06T11/00 ,  G06T11/60 ,  G06V10/77 ,  G06V10/774 ,  G06V10/82 ,  G06V20/40

CPC classification number: G06T11/001 ,  G06T11/60 ,  G06V10/7715 ,  G06V10/774 ,  G06V10/82 ,  G06V20/46 ,  G06V2201/10

Abstract: Digital content stylization techniques are described that leverage a neural photofinisher to generate stylized digital images. In one example, the neural photofinisher is implemented as part of a stylization system to train a neural network to perform digital image style transfer operations using reference digital content as training data. The training includes calculating a style loss term that identifies a particular visual style of the reference digital content. Once trained, the stylization system receives a digital image and generates a feature map of a scene depicted by the digital image. Based on the feature map as well as the style loss, the stylization system determines visual parameter values to apply to the digital image to incorporate a visual appearance of the particular visual style. The stylization system generates the stylized digital image by applying the visual parameter values to the digital image automatically and without user intervention.