Patents by Inventor Gregory ROGEZ
Gregory ROGEZ has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20240412726Abstract: A method and system for text-based pose editing to generate a new pose from an initial pose and user-generated text includes an user input device for inputting the initial pose and the user-generated text; a variational auto-encoder configured to receive the initial pose; a text conditioning pipeline configured to receive the user-generated text; a fusing module configured to produce parameters for a prior Gaussian distribution Np; a pose decoder configured to sample the Gaussian distribution Np and generate, therefrom, the new pose; and an output device to communicate the generated new pose to a user. The variational auto-encoder and the text conditioning pipeline are trained using a PoseFix dataset, wherein the PoseFix dataset includes triplets having a source pose, a target pose, and text modifier.Type: ApplicationFiled: December 9, 2023Publication date: December 12, 2024Applicants: Naver Corporation, Naver Labs CorporationInventors: Ginger Delmas, Philippe Weinzaepfel, Francesc Moreno-noguer, Gregory Rogez
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Publication number: 20240412041Abstract: A method and system for automatically building a three-dimensional pose dataset for use in text to pose retrieval or text to pose generation for a class of poses includes (a) inputting three-dimensional keypoint coordinates of class-centric poses; (b) extracting, from the inputted three-dimensional keypoint coordinates of class-centric poses, posecodes, the posecodes representing a relation between a specific set of joints; (c) selecting extracted posecodes to obtain a discriminative description; (d) aggregating selected posecodes that share semantic information; (e) converting the aggregated posecodes by electronically obtaining individual descriptions by plugging each posecode information into one template sentence, picked at random from a set of possible templates for a given posecode category; (f) concatenating the individual descriptions in random order, using random pre-defined transitions; and (g) mapping the concatenated individual descriptions to class-centric poses to create the three-dimensional poType: ApplicationFiled: October 3, 2023Publication date: December 12, 2024Applicants: Naver Corporation, Naver Labs CorporationInventors: Ginger Delmas, Philippe Weinzaepfel, Thomas Lucas, Francesc Moreno-noguer, Gregory Rogez
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Patent number: 12165401Abstract: A computer-implemented method of recognition of actions performed by individuals includes: by one or more processors, obtaining images including at least a portion of an individual; by the one or more processors, based on the images, generating implicit representations of poses of the individual in the images; and by the one or more processors, determining an action performed by the individual and captured in the images by classifying the implicit representations of the poses of the individual.Type: GrantFiled: March 13, 2023Date of Patent: December 10, 2024Assignee: NAVER CORPORATIONInventors: Philippe Weinzaepfel, Gregory Rogez
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Patent number: 12154227Abstract: A system includes: a feature module configured to generate a feature map based on a single image taken from a point of view (POV) including a human based on features of the human visible in the image and non-visible features of the human; a pixel features module configured to generate pixel features based on the feature map and a target POV; a feature mesh module configured to generate a feature mesh for the human based on the feature map; a geometry module configured to: generate voxel features based on the feature mesh; and generate a density value based on the voxel and pixel features; a texture module configured to generate RGB colors for pixels based on the density value and the pixel features; and a rendering module configured to generate a three dimensional rendering of the human from the target POV based on the RGB colors and the density value.Type: GrantFiled: December 16, 2022Date of Patent: November 26, 2024Assignees: NAVER CORPORATION, SEOUL NATIONAL UNIVERSITY R&DB FOUNDATIONInventors: Hongsuk Choi, Gyeongsik Moon, Vincent Leroy, KyoungMu Lee, Grégory Rogez
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Publication number: 20240144019Abstract: A training system includes: a model; and a training module configured to: construct a first pair of images of at least a first portion of a first human captured at different times; construct a second pair of images of at least a second portion of a second human captured at the same time from different points of view; input the first and second pairs of images to the model; the model configured to: generate first and second reconstructed images of the at least the first portion of the first human based on the first and second pairs, respectively, and the training module is configured to selectively adjust one or more parameters of the model based on: the first reconstructed image and the second reconstructed image.Type: ApplicationFiled: August 29, 2023Publication date: May 2, 2024Applicants: NAVER CORPORATION, NAVER LABS CORPORATIONInventors: Philippe WEINZAEPFEL, Vincent Leroy, Romain Brègier, Yohann Cabon, Thomas Lucas, Leonid Antsfield, Boris Chidlovskii, Gabriela Csurka Khedari, Jèrôme Revaud, Matthieu Armando, Fabien Baradel, Salma Galaaoui, Gregory Rogez
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Publication number: 20240127462Abstract: A motion generation system includes: a model configured to generate latent indices for a sequence of images including an entity performing an action based on an action label and a duration of the sequence; and a decoder module configured to: decode the latent indices and to generate the sequence of images including the entity performing the action based on the latent indices; and output the sequence of images including the entity performing the action to a display control module configured to display the sequence of images including the entity performing the action sequentially on a display.Type: ApplicationFiled: September 29, 2022Publication date: April 18, 2024Applicants: NAVER CORPORATION, NAVER LABS CORPORATIONInventors: Thomas LUCAS, Fabien BARADEL, Philippe WEINZAEPFEL, Gregory ROGEZ
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Publication number: 20240046569Abstract: A system includes: a feature module configured to generate a feature map based on a single image taken from a point of view (POV) including a human based on features of the human visible in the image and non-visible features of the human; a pixel features module configured to generate pixel features based on the feature map and a target POV; a feature mesh module configured to generate a feature mesh for the human based on the feature map; a geometry module configured to: generate voxel features based on the feature mesh; and generate a density value based on the voxel and pixel features; a texture module configured to generate RGB colors for pixels based on the density value and the pixel features; and a rendering module configured to generate a three dimensional rendering of the human from the target POV based on the RGB colors and the density value.Type: ApplicationFiled: December 16, 2022Publication date: February 8, 2024Applicants: NAVER CORPORATION, NAVER LABS CORPORATION, SEOUL NATIONAL UNIVERSITY R&DB FOUNDATIONInventors: Hongsuk CHOI, Gyeongsik MOON, Vincent LEROY, KyoungMu LEE, Grégory ROGEZ
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Publication number: 20230215160Abstract: A computer-implemented method of recognition of actions performed by individuals includes: by one or more processors, obtaining images including at least a portion of an individual by the one or more processors, based on the images, generating implicit representations of poses of the individual in the images; and by the one or more processors, determining an action performed by the individual and captured in the images by classifying the implicit representations of the poses of the individual.Type: ApplicationFiled: March 13, 2023Publication date: July 6, 2023Applicant: Naver CorporationInventors: Philippe WEINZAEPFEL, Gregory Rogez
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Patent number: 11651608Abstract: A system for generating whole body poses includes: a body regression module configured to generate a first pose of a body of an animal in an input image by regressing from a stored body anchor pose; a face regression module configured to generate a second pose of a face of the animal in the input image by regressing from a stored face anchor pose; an extremity regression module configured to generate a third pose of an extremity of the animal in the input image by regressing from a stored extremity anchor pose; and a pose module configured to generate a whole body pose of the animal in the input image based on the first pose, the second pose, and the third pose.Type: GrantFiled: September 13, 2022Date of Patent: May 16, 2023Assignees: NAVER CORPORATION, NAVER LABS CORPORATIONInventors: Philippe Weinzaepfel, Romain Bregier, Hadrien Combaluzier, Vincent Leroy, Gregory Rogez
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Publication number: 20230119559Abstract: A training system includes: a neural network model configured to determine three-dimensional coordinates of joints, respectively, representing poses of animals in images, where the neural network model is trained using a first training dataset including: images including animals; and coordinates of joints of the animals in the images, respectively; and a training module configured to, after the training of the neural network model using the first training dataset, train the neural network model using a second training dataset including motion capture data, where the motion capture data does not include images of animals and includes measured coordinates at points, respectively, on animals.Type: ApplicationFiled: October 4, 2021Publication date: April 20, 2023Applicants: NAVER CORPORATION, NAVER LABS CORPORATIONInventors: Fabien BARADEL, Romain BREGIER, Thibault GROUEIX, Ioannis KALANTIDIS, Philippe WEINZAEPFEL, Gregory ROGEZ
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Patent number: 11625953Abstract: A computer-implemented method of recognition of actions performed by individuals includes: by one or more processors, obtaining images including at least a portion of an individual; by the one or more processors, based on the images, generating implicit representations of poses of the individual in the images; and by the one or more processors, determining an action performed by the individual and captured in the images by classifying the implicit representations of the poses of the individual.Type: GrantFiled: June 17, 2020Date of Patent: April 11, 2023Assignee: NAVER CORPORATIONInventors: Philippe Weinzaepfel, Gregory Rogez
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Publication number: 20230082941Abstract: A method for processing a new sample in a data stream for updating a machine learning model configured for performing a task. The machine learning model is implemented by a processor in communication with a memory storing previous samples. The new sample is received, and the machine learning model is trained using combined samples including the new sample and the previous samples. The new sample is stored or not stored in the memory based on distances between the samples in an embedding space learned by the machine learning model.Type: ApplicationFiled: September 3, 2021Publication date: March 16, 2023Inventors: Riccardo VOLPI, Ioannis KALANTIDIS, Diane LARLUS, César DE SOUZA, Gregory ROGEZ
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Publication number: 20230053716Abstract: A method of semi-supervised learning includes inputting an image; generating a weak augmentation version and a strong augmentation version of the inputted image; predicting a class of the weak augmentation version of the inputted image; determining if the predicted class of the weak augmentation version of the inputted image is confident; using a pseudo-label to train a model using the strong augmentation version of the inputted image when the predicted class of the weak augmentation version of the selected image is confident; and using a self-supervised loss based on deep clustering to train a model using the strong augmentation version of the selected image when the predicted class of the weak augmentation version of the selected image is not confident.Type: ApplicationFiled: March 30, 2022Publication date: February 23, 2023Applicant: Naver CorporationInventors: Philippe Weinzaepfel, Gregory Rogez, Thomas Lucas
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Publication number: 20230015984Abstract: A system for generating whole body poses includes: a body regression module configured to generate a first pose of a body of an animal in an input image by regressing from a stored body anchor pose; a face regression module configured to generate a second pose of a face of the animal in the input image by regressing from a stored face anchor pose; an extremity regression module configured to generate a third pose of an extremity of the animal in the input image by regressing from a stored extremity anchor pose; and a pose module configured to generate a whole body pose of the animal in the input image based on the first pose, the second pose, and the third pose.Type: ApplicationFiled: September 13, 2022Publication date: January 19, 2023Applicants: NAVER CORPORATION, NAVER LABS CORPORATIONInventors: Philippe WEINZAEPFEL, Romain Bregier, Hadrien Combaluzier, Vincent Leroy, Gregory Rogez
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Publication number: 20220402125Abstract: Method for determining a grasping hand model suitable for grasping an object by receiving an image including at least one object; obtaining an object model estimating a pose and shape of the object from the image of the object; selecting a grasp class from a set of grasp classes by means of a neural network, with a cross entropy loss, thus, obtaining a set of parameters defining a coarse grasping hand model; refining the coarse grasping hand model, by minimizing loss functions referring to the parameters of the hand model for obtaining an operable grasping hand model while minimizing the distance between the finger of the hand model and the surface of the object and preventing interpenetration; and obtaining a mesh of the hand represented by the enhanced set of parameters.Type: ApplicationFiled: June 6, 2022Publication date: December 22, 2022Applicant: Naver Labs CorporationInventors: Francesc Moreno Noguer, Guillem Alenyà Ribas, Enric Corona Puyane, Albert Pumarola Peris, Grégory Rogez
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Patent number: 11494932Abstract: A system for generating whole body poses includes: a body regression module configured to generate a first pose of a body of an animal in an input image by regressing from a stored body anchor pose; a face regression module configured to generate a second pose of a face of the animal in the input image by regressing from a stored face anchor pose; an extremity regression module configured to generate a third pose of an extremity of the animal in the input image by regressing from a stored extremity anchor pose; and a pose module configured to generate a whole body pose of the animal in the input image based on the first pose, the second pose, and the third pose.Type: GrantFiled: June 2, 2020Date of Patent: November 8, 2022Assignees: NAVER CORPORATION, NAVER LABS CORPORATIONInventors: Philippe Weinzaepfel, Romain Bregier, Hadrien Combaluzier, Vincent Leroy, Gregory Rogez
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Publication number: 20220172048Abstract: Methods for training a neural network model for sequentially learning a plurality of domains associated with a task. At least one set of auxiliary model parameters is determined by simulating at least one first optimization step based on a set of current model parameters and at least one auxiliary domain associated with a primary domain comprising one or more data points. A set of primary model parameters is determined by performing a second optimization step based on the current model parameters and the primary domain and on the at least one set of auxiliary model parameters and the primary domain and/or the auxiliary domain. The model is updated with the set of primary model parameters.Type: ApplicationFiled: October 29, 2021Publication date: June 2, 2022Inventors: Diane LARLUS, Riccardo VOLPI, Gregory ROGEZ
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Publication number: 20220009091Abstract: Method for determining a grasping hand model suitable for grasping an object by obtaining a first RGB image including at least one object; obtaining an object model estimating a pose and shape of said object from the first image of the object; selecting a grasp taxonomy from a set of grasp taxonomies by means of a Convolutional Neural Network, with a cross entropy loss, thus, obtaining a set of parameters defining a coarse grasping hand model; refining the coarse grasping hand model, by minimizing loss functions referring to the parameters of the hand model for obtaining an operable grasping hand model while minimizing the distance between the finger of the hand model and the surface of the object and preventing interpenetration; and obtaining a mesh of the hand represented by the enhanced set of parameters.Type: ApplicationFiled: June 8, 2021Publication date: January 13, 2022Applicants: Naver France, Consejo Superior de Investigaciones Cientificas (CSIC), Universitatpolitècnica De Catalunya Plaça d'Eusebi Güell 6 Edifici Vertex, Planta 1Inventors: Francesc Moreno Noguer, Guillem Alenyà Ribas, Enric Corona Puyane, Albert Pumarola Peris, Grégory Rogez
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Publication number: 20210374989Abstract: A system for generating whole body poses includes: a body regression module configured to generate a first pose of a body of an animal in an input image by regressing from a stored body anchor pose; a face regression module configured to generate a second pose of a face of the animal in the input image by regressing from a stored face anchor pose; an extremity regression module configured to generate a third pose of an extremity of the animal in the input image by regressing from a stored extremity anchor pose; and a pose module configured to generate a whole body pose of the animal in the input image based on the first pose, the second pose, and the third pose.Type: ApplicationFiled: June 2, 2020Publication date: December 2, 2021Applicants: NAVER CORPORATION, NAVER LABS CORPORATIONInventors: Philippe WEINZAEPFEL, Romain BREGIER, Hadrien COMBALUZIER, Vincent LEROY, Gregory ROGEZ
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Publication number: 20210073525Abstract: A computer-implemented method of recognition of actions performed by individuals includes: by one or more processors, obtaining images including at least a portion of an individual; by the one or more processors, based on the images, generating implicit representations of poses of the individual in the images; and by the one or more processors, determining an action performed by the individual and captured in the images by classifying the implicit representations of the poses of the individual.Type: ApplicationFiled: June 17, 2020Publication date: March 11, 2021Applicant: NAVER CORPORATIONInventors: Philippe WEINZAEPFEL, Gregory ROGEZ