公开(公告)号：US20240371081A1

公开(公告)日：2024-11-07

申请号：US18688278

申请日：2022-04-13

Applicant: Google LLC

Inventor： Mark Jeffrey Matthews ,  Daniel Jonathan Rebain ,  Dmitry Lagun ,  Andrea Tagliasacchi

IPC: G06T15/20 ,  G06T15/08 ,  G06V10/774 ,  G06V40/16

Abstract: Systems and methods for learning spaces of three-dimensional shape and appearance from datasets of single-view images can be utilized for generating view renderings of a variety of different objects and/or scenes. The systems and methods can be able to learn effectively from unstructured. “in-the-wild” data, without incurring the high cost of a full-image discriminator, and while avoiding problems such as mode-dropping that are inherent to adversarial methods.

公开(公告)号：US20240135492A1

公开(公告)日：2024-04-25

申请号：US18379519

申请日：2023-10-12

Applicant: Google LLC

Inventor： Cristina Nader Vasconcelos ,  Ahmet Cengiz Oztireli ,  Andrea Tagliasacchi ,  Kevin Jordan Swersky ,  Mark Jeffrey Matthews ,  Milad Olia Hashemi

IPC: G06T3/40 ,  G06T5/20 ,  G06V10/771

CPC classification number: G06T3/4053 ,  G06T5/20 ,  G06V10/771 ,  G06T2207/10024 ,  G06T2207/20084

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing an input image using a super-resolution neural network to generate an up-sampled image that is a higher resolution version of the input image. In one aspect, a method comprises: processing the input image using an encoder subnetwork of the super-resolution neural network to generate a feature map; generating an updated feature map, comprising, for each spatial position in the updated feature map: applying a convolutional filter to the feature map to generate a plurality of features corresponding to the spatial position in the updated feature map, wherein the convolutional filter is parametrized by a set of convolutional filter parameters that are generated by processing data representing the spatial position using a hyper neural network; and processing the updated feature map using a projection subnetwork of the super-resolution neural network to generate the up-sampled image.

公开(公告)号：US20250166136A1

公开(公告)日：2025-05-22

申请号：US18957367

申请日：2024-11-22

Applicant: Google LLC

Inventor： Mark Jeffrey Matthews ,  Prafull Sharma ,  Dmitry Lagun ,  Xuhui Jia ,  Yuanzhen Li ,  Varun Jampani ,  William Tafel Freeman

IPC: G06T5/60 ,  G06T5/73 ,  G06T11/60

Abstract: Provided are systems and methods for controlling material attributes such as roughness, metallic, albedo, and transparency in real images. This method leverages the generative prior of text-to-image models known for their photorealistic capabilities, offering an alternative to traditional rendering pipelines. As one example, the technology can be used to alter the appearance of an object in an image, making it appear more metallic or changing its roughness to create a more matte or glossy finish. This can be particularly useful in various fields where the ability to manipulate the appearance of products in images can be a powerful tool.