公开(公告)号：US20250054115A1

公开(公告)日：2025-02-13

申请号：US18232212

申请日：2023-08-09

Applicant: Adobe Inc.

Inventor： Zhe LIN ,  Yuqian ZHOU ,  Sohrab AMIRGHODSI ,  Qing LIU ,  Elya SHECHTMAN ,  Connelly BARNES ,  Haitian ZHENG

IPC: G06T5/00 ,  G06T3/40 ,  G06T7/11

Abstract: Various disclosed embodiments are directed to resizing, via down-sampling and up-sampling, a high-resolution input image in order to meet machine learning model low-resolution processing requirements, while also producing a high-resolution output image for image inpainting via a machine learning model. Some embodiments use a refinement model to refine the low-resolution inpainting result from the machine learning model such that there will be clear content with high resolution both inside and outside of the mask region in the output. Some embodiments employ new model architecture for the machine learning model that produces the inpainting result—an advanced Cascaded Modulated Generative Adversarial Network (CM-GAN) that includes Fast Fourier Convolution (FCC) layers at the skip connections between the encoder and decoder.

公开(公告)号：US20240371002A1

公开(公告)日：2024-11-07

申请号：US18312858

申请日：2023-05-05

Applicant: Adobe Inc.

Inventor： Brian PRICE ,  Tai-Yu PAN ,  Qing LIU

IPC: G06T7/10 ,  G06V10/762 ,  G06V10/774 ,  G06V10/776 ,  G06V10/82

Abstract: A method includes receiving an object mask of an object in an image. The method further includes generating a mask of a sub-object in the image using a machine learning model configured to receive the mask of the object. A first branch of the machine learning model predicts whether a pixel of the image belongs to a sub-object.