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公开(公告)号：US20240265610A1
公开(公告)日：2024-08-08
申请号：US18164538
申请日：2023-02-03
发明人： Marek Adam KOWALSKI , Stephan Joachim GARBIN , Virginia ESTELLERS CASAS , Julien Pascal Christophe VALENTIN , Kacper KANIA
CPC分类号： G06T13/40 , G06N3/08 , G06T15/06 , G06T15/08 , G06T19/20 , G06T2219/2012 , G06T2219/2016 , G06T2219/2021
摘要： A cage of primitive 3D elements and associated animation data is received. Compute a ray from a virtual camera through a pixel into the cage animated according to the animation data and compute a plurality of samples on the ray. Compute a transformation of the samples into a canonical cage. For each transformed sample, query a plurality of learnt radiance field parameterizations, each learnt on a different deformed state of the 3D scene to obtain color values for each learnt radiance field. For each transformed sample, query a learnt radiance field parameterization of the 3D scene to obtain an opacity value. Compute, for each transformed sample, a weighted combination of the color values, wherein the weights are related to the local features. A volume rendering method is applied to the weighted combinations of the color and the opacity values producing a pixel value.
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公开(公告)号：US20230281945A1
公开(公告)日：2023-09-07
申请号：US17851933
申请日：2022-06-28
发明人： Thomas Joseph CASHMAN , Erroll William WOOD , Martin DE LA GORCE , Tadas BALTRUSAITIS , Daniel Stephen WILDE , Jingjing SHEN , Matthew Alastair JOHNSON , Julien Pascal Christophe VALENTIN
摘要： Keypoints are predicted in an image. A neural network is executed that is configured to predict each of the keypoints as a 2D random variable, normally distributed with a 2D position and 2×2 covariance matrix. The neural network is trained to maximize a log-likelihood that samples from each of the predicted keypoints equal a ground truth. The trained neural network is used to predict keypoints of an image without generating a heatmap.
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公开(公告)号：US20230326238A1
公开(公告)日：2023-10-12
申请号：US17719335
申请日：2022-04-12
IPC分类号： G06V40/16
CPC分类号： G06V40/165
摘要： A neural optimizer is disclosed that is easily applicable to different fitting problems, can run at interactive rates without requiring significant efforts, does not require hand crafted priors, carries over information about previous iterations of the solve, controls the learning rate of each parameter independently for robustness and convergence speed, and combines updates from gradient descent and from a method capable of very quickly reducing the fitting energy. A neural fitter estimates the values of the parameters Θ by iteratively updating an initial estimate Θ0.
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公开(公告)号：US20230316552A1
公开(公告)日：2023-10-05
申请号：US17713038
申请日：2022-04-04
发明人： JingJing SHEN , Erroll William WOOD , Toby SHARP , Ivan RAZUMENIC , Tadas BALTRUSAITIS , Julien Pascal Christophe VALENTIN , Predrag JOVANOVIC
IPC分类号： G06T7/55 , H04N5/225 , G06V10/82 , G06T7/70 , G06T7/20 , G06T19/20 , G06V10/22 , G06V20/64 , G06T17/00 , G01S17/894 , G01S17/86
CPC分类号： G06T7/55 , H04N5/2258 , G06V10/82 , G06T7/70 , G06T7/20 , G06T19/20 , G06V10/22 , G06V20/647 , G06T17/00 , G01S17/894 , G01S17/86 , G06T2207/10024 , G06T2207/10028 , G06T2207/10021 , G06T2219/2016 , G06T2219/2004 , G06T2210/56 , G06T2200/08
摘要： The techniques described herein disclose a system that is configured to detect and track the three-dimensional pose of an object (e.g., a head-mounted display device) in a color image using an accessible three-dimensional model of the object. The system uses the three-dimensional pose of the object to repair pixel depth values associated with a region (e.g., a surface) of the object that is composed of material that absorbs light emitted by a time-of-flight depth sensor to determine depth. Consequently, a color-depth image (e.g., a Red-Green-Blue-Depth image or RGB-D image) can be produced that does not include dark holes on and around the region of the object that is composed of material that absorbs light emitted by the time-of-flight depth sensor.
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5.发明申请公开(公告)号：US20220343133A1
公开(公告)日：2022-10-27
申请号：US17862699
申请日：2022-07-12
摘要： A system receives input from a user to initiate a process of generating a holodouble of the user. The system obtains image data of the user and deconstructs the image data to obtain a set of sparse data that identifies one or more attributes associated with the image data the user. The system uses a holodouble training model to generate and train the holodouble of the user based on the set of sparse data and obtained image data. The system renders a representation of the holodouble to the user concurrently while capturing new image data of the user, receives input from the user comprising approval of the holodouble, and completes training of the holodouble by saving the holodouble for subsequent use. The subsequent use includes one or more remote visual communication sessions.
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公开(公告)号：US20240037829A1
公开(公告)日：2024-02-01
申请号：US17933453
申请日：2022-09-19
发明人： Julien Pascal Christophe VALENTIN , Virginia ESTELLERS CASAS , Shideh REZAEIFAR , Jingjing SHEN , Stanislaw Kacper SZYMANOWICZ , Stephan Joachim GARBIN , Marek Adam KOWALSKI , Matthew Alastair JOHNSON
摘要： To compute an image of a dynamic 3D scene comprising a 3D object, a description of a deformation of the 3D object is received, the description comprising a cage of primitive 3D elements and associated animation data from a physics engine or an articulated object model. For a pixel of the image the method computes a ray from a virtual camera through the pixel into the cage animated according to the animation data and computes a plurality of samples on the ray. Each sample is a 3D position and view direction in one of the 3D elements. The method computes a transformation of the samples into a canonical cage. For each transformed sample, the method queries a learnt radiance field parameterization of the 3D scene to obtain a color value and an opacity value. A volume rendering method is applied to the color and opacity values producing a pixel value of the image.
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公开(公告)号：US20230281863A1
公开(公告)日：2023-09-07
申请号：US17852175
申请日：2022-06-28
发明人： Julien Pascal Christophe VALENTIN , Erroll William WOOD , Thomas Joseph CASHMAN , Martin de LA GORCE , Tadas BALTRUSAITIS , Daniel Stephen WILDE , Jingjing SHEN , Matthew Alastair JOHNSON , Charles Thomas HEWITT , Nikola MILOSAVLJEVIC , Stephan Joachim GARBIN , Toby SHARP , Ivan STOJILJKOVIC
CPC分类号： G06T7/73 , G06T7/344 , G06T17/00 , G06T19/20 , G06T2207/20081 , G06T2207/20084 , G06T2219/2004 , G06T2207/30201
摘要： Keypoints are predicted in an image. Predictions are generated for each of the keypoints of an image as a 2D random variable, normally distributed with location (x, y) and standard deviation sigma. A neural network is trained to maximize a log-likelihood that samples from each of the predicted keypoints equal a ground truth. The trained neural network is used to predict keypoints of an image without generating a heatmap.
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