公开(公告)号：US10412316B2

公开(公告)日：2019-09-10

申请号：US15392452

申请日：2016-12-28

Applicant: Google LLC

Inventor： Michael Rubinstein ,  William Freeman ,  Ce Liu

IPC: H04N5/262 ,  H04N5/222 ,  H04N5/232 ,  G06T7/30 ,  G06T7/90 ,  G06T5/00 ,  G06T5/20 ,  G06T5/50

Abstract: The present disclosure relates to systems and methods for image capture. Namely, an image capture system may include a camera configured to capture images of a field of view, a display, and a controller. An initial image of the field of view from an initial camera pose may be captured. An obstruction may be determined to be observable in the field of view. Based on the obstruction, at least one desired camera pose may be determined. The at least one desired camera pose includes at least one desired position of the camera. A capture interface may be displayed, which may include instructions for moving the camera to the at least one desired camera pose. At least one further image of the field of view from the at least one desired camera pose may be captured. Captured images may be processed to remove the obstruction from a background image.

公开(公告)号：US20240013497A1

公开(公告)日：2024-01-11

申请号：US18252118

申请日：2020-12-21

Applicant: Google LLC

Inventor： Deqing Sun ,  Varun Jampani ,  Gengshan Yang ,  Daniel Vlasic ,  Huiwen Chang ,  Forrester H. Cole ,  Ce Liu ,  William Tafel Freeman

IPC: G06T19/20 ,  G06T7/55 ,  G06T17/20 ,  G06T7/20 ,  G06T7/40

CPC classification number: G06T19/20 ,  G06T7/55 ,  G06T17/20 ,  G06T7/20 ,  G06T7/40 ,  G06T2207/30244 ,  G06T2207/10016 ,  G06T2207/20084 ,  G06T2219/2021 ,  G06T2207/20081

Abstract: A computing system and method can be used to render a 3D shape from one or more images. In particular, the present disclosure provides a general pipeline for learning articulated shape reconstruction from images (LASR). The pipeline can reconstruct rigid or nonrigid 3D shapes. In particular, the pipeline can automatically decompose non-rigidly deforming shapes into rigid motions near rigid-bones. This pipeline incorporates an analysis-by-synthesis strategy and forward-renders silhouette, optical flow, and color images which can be compared against the video observations to adjust the internal parameters of the model. By inverting a rendering pipeline and incorporating optical flow, the pipeline can recover a mesh of a 3D model from the one or more images input by a user.

公开(公告)号：US20230260145A1

公开(公告)日：2023-08-17

申请号：US18135678

申请日：2023-04-17

Applicant: Google LLC

Inventor： Tali Dekel ,  Forrester Cole ,  Ce Liu ,  William Freeman ,  Richard Tucker ,  Noah Snavely ,  Zhengqi Li

IPC: G06T7/579 ,  G06T7/246 ,  G06T7/73

CPC classification number: G06T7/579 ,  G06T7/246 ,  G06T7/73 ,  G06T2207/10016 ,  G06T2207/30244 ,  G06T2207/20081 ,  G06T2207/10028

Abstract: A method includes obtaining a reference image and a target image each representing an environment containing moving features and static features. The method also includes determining an object mask configured to mask out the moving features and preserves the static features in the target image. The method additionally includes determining, based on motion parallax between the reference image and the target image, a static depth image representing depth values of the static features in the target image. The method further includes generating, by way of a machine learning model, a dynamic depth image representing depth values of both the static features and the moving features in the target image. The model is trained to generate the dynamic depth image by determining depth values of at least the moving features based on the target image, the object mask, and the static depth image.

公开(公告)号：US11663733B2

公开(公告)日：2023-05-30

申请号：US17656165

申请日：2022-03-23

Applicant: Google LLC

Inventor： Tali Dekel ,  Forrester Cole ,  Ce Liu ,  William Freeman ,  Richard Tucker ,  Noah Snavely ,  Zhengqi Li

IPC: G06T7/579 ,  G06T7/246 ,  G06T7/73

CPC classification number: G06T7/579 ,  G06T7/246 ,  G06T7/73 ,  G06T2207/10016 ,  G06T2207/10028 ,  G06T2207/20081 ,  G06T2207/30244

Abstract: A method includes obtaining a reference image and a target image each representing an environment containing moving features and static features. The method also includes determining an object mask configured to mask out the moving features and preserves the static features in the target image. The method additionally includes determining, based on motion parallax between the reference image and the target image, a static depth image representing depth values of the static features in the target image. The method further includes generating, by way of a machine learning model, a dynamic depth image representing depth values of both the static features and the moving features in the target image. The model is trained to generate the dynamic depth image by determining depth values of at least the moving features based on the target image, the object mask, and the static depth image.

公开(公告)号：US20230111326A1

公开(公告)日：2023-04-13

申请号：US17792062

申请日：2020-01-13

Applicant: GOOGLE LLC

Inventor： Ruohan Zhan ,  Feng Yang ,  Xiyang Luo ,  Peyman Milanfar ,  Huiwen Chang ,  Ce Liu

IPC: G06T1/00 ,  G06T9/00 ,  G06V10/75 ,  G06V10/82

Abstract: Methods, systems, and computer programs encoded on a computer storage medium, that relate to extracting digital watermarks from images, irrespective of distortions introduced into these images. Methods can include inputting a first data item into a channel encoder that can generate a first encoded data item that is greater in length than the first data item and that (1) includes the input data item and (2) new data this is redundant of the input data item. Based on the first encoded data item and a first image, an encoder model can generate a first encoded image into which the first encoded data is embedded as a digital watermark. A decoder model can decode the first encoded data item to generate a second data, which can be decoded by the channel decoder to generate data that is predicted to be the first data.

公开(公告)号：US11481941B2

公开(公告)日：2022-10-25

申请号：US17009194

申请日：2020-09-01

Applicant: Google LLC

Inventor： Mikaël Bonnevie ,  Yuanzhen Li ,  Ce Liu

IPC: G06T11/60 ,  G06T7/194 ,  G06T5/00 ,  G06T3/40

Abstract: A multimedia communication system and computer-implemented method for transmitting auxiliary display content to an end-user communication device to be rendered on a display device with a special effect to emphasize an image included in the auxiliary display content, comprising a processor and a transmitter. The processor can be arranged to analyze image data included in an auxiliary display content, detect an object image or a background image in the auxiliary display content based on the analysis of the image data, determine a special effect based on the analysis of the image data, and apply the special effect to the auxiliary display content to modify display properties for the auxiliary display content such that the object image is emphasized or pops-out. The transmitter can be arranged to send the auxiliary display content with modified display properties to an end-user communication device. The special effect can comprise a non-customization special effect, a simple foreground special effect or a selective foreground special effect.

公开(公告)号：US20220215568A1

公开(公告)日：2022-07-07

申请号：US17656165

申请日：2022-03-23

Applicant: Google LLC

Inventor： Tali Dekel ,  Forrester Cole ,  Ce Liu ,  William Freeman ,  Richard Tucker ,  Noah Snavely ,  Zhengqi Li

IPC: G06T7/579 ,  G06T7/246 ,  G06T7/73

Abstract: A method includes obtaining a reference image and a target image each representing an environment containing moving features and static features. The method also includes determining an object mask configured to mask out the moving features and preserves the static features in the target image. The method additionally includes determining, based on motion parallax between the reference image and the target image, a static depth image representing depth values of the static features in the target image. The method further includes generating, by way of a machine learning model, a dynamic depth image representing depth values of both the static features and the moving features in the target image. The model is trained to generate the dynamic depth image by determining depth values of at least the moving features based on the target image, the object mask, and the static depth image.

公开(公告)号：US20220148299A1

公开(公告)日：2022-05-12

申请号：US17438687

申请日：2019-07-19

Applicant: Google LLC

Inventor： Mikael Pierre Bonnevie ,  Aaron Maschinot ,  Aaron Sarna ,  Shuchao Bi ,  Jingbin Wang ,  Michael Spencer Krainin ,  Wenchao Tong ,  Dilip Krishnan ,  Haifeng Gong ,  Ce Liu ,  Hossein Talebi ,  Raanan Sayag ,  Piotr Teterwak

IPC: G06V10/82 ,  G06N3/04 ,  G06T7/10

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating realistic extensions of images. In one aspect, a method comprises providing an input that comprises a provided image to a generative neural network having a plurality of generative neural network parameters. The generative neural network processes the input in accordance with trained values of the plurality of generative neural network parameters to generate an extended image. The extended image has (i) more rows, more columns, or both than the provided image, and (ii) is predicted to be a realistic extension of the provided image. The generative neural network is trained using an adversarial loss objective function.

公开(公告)号：US11315274B2

公开(公告)日：2022-04-26

申请号：US16578215

申请日：2019-09-20

Applicant: Google LLC

Inventor： Tali Dekel ,  Forrester Cole ,  Ce Liu ,  William Freeman ,  Richard Tucker ,  Noah Snavely ,  Zhengqi Li

IPC: G06T7/00 ,  G06T7/579 ,  G06T7/246 ,  G06T7/73

Abstract: A method includes obtaining a reference image and a target image each representing an environment containing moving features and static features. The method also includes determining an object mask configured to mask out the moving features and preserves the static features in the target image. The method additionally includes determining, based on motion parallax between the reference image and the target image, a static depth image representing depth values of the static features in the target image. The method further includes generating, by way of a machine learning model, a dynamic depth image representing depth values of both the static features and the moving features in the target image. The model is trained to generate the dynamic depth image by determining depth values of at least the moving features based on the target image, the object mask, and the static depth image.