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公开(公告)号：US20190080474A1
公开(公告)日：2019-03-14
申请号：US16188255
申请日：2018-11-12
Applicant: Google LLC
Inventor： Dmitry Lagun , Junfeng He , Pingmei Xu
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for characterizing a gaze position of a user in a query image. One of the methods includes obtaining a query image of a user captured by a camera of a mobile device; obtaining device characteristics data specifying (ii) characteristics of the mobile device, (ii) characteristics of the camera of the mobile device, or (iii) both; and processing a neural network input comprising (i) one or more images derived from the query image and (ii) the device characteristics data using a gaze prediction neural network, wherein the gaze prediction neural network is configured to, at run time and after the gaze prediction neural network has been trained, process the neural network input to generate a neural network output that characterizes a gaze position of the user in the query image.
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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
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.
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公开(公告)号：US20250037353A1
公开(公告)日：2025-01-30
申请号：US18714862
申请日：2022-01-13
Applicant: Google LLC
Inventor： Wen-Sheng Chu , Dmitry Lagun , Ioannis Daras , Abhishek Kumar
Abstract: Systems and methods for training a generative neural radiance field model can include geometric regularization. Geometric regularization can involve the utilization of reference geometry data and/or an output of a surface prediction model. The geometry regularization can train the generative neural radiance field model to mitigate artifact generation by limiting a distribution considered for color value prediction and density value prediction to a range associated with a realistic geometry range.
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公开(公告)号：US20240126365A1
公开(公告)日：2024-04-18
申请号：US18279117
申请日：2021-04-21
Applicant: Google LLC
Inventor： Dmitry Lagun , Gautam Prasad , Pezhman Firoozfam , Jimin Pi
CPC classification number: G06F3/013 , G06T7/70 , G06T11/60 , G06T2207/20081 , G06T2207/20084
Abstract: The technology relates to methods and systems for implicit calibration for gaze tracking. This can include receiving, by a neural network module, display content that is associated with presentation on a display screen (1202). The neural network module may also receive uncalibrated gaze information, in which the uncalibrated gaze information includes an uncalibrated gaze trajectory that is associated with a viewer gaze of the display content on the display screen (1204). A selected function is applied by the neural network module to the uncalibrated gaze information and the display content to generate a user-specific gaze function (1206). The user-specific gaze function has one or more personalized parameters. And the neural network module can then apply the user-specific gaze function to the uncalibrated gaze information to generate calibrated gaze information associated with the display content on the display screen (1208). Training and testing information may alternatively be created for implicit gaze calibration (1000).
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