Patents by Inventor Philippe Weinzaepfel

Philippe Weinzaepfel 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).

  • Publication number: 20240412041
    Abstract: 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 po
    Type: Application
    Filed: October 3, 2023
    Publication date: December 12, 2024
    Applicants: Naver Corporation, Naver Labs Corporation
    Inventors: Ginger Delmas, Philippe Weinzaepfel, Thomas Lucas, Francesc Moreno-noguer, Gregory Rogez
  • Publication number: 20240412726
    Abstract: 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: Application
    Filed: December 9, 2023
    Publication date: December 12, 2024
    Applicants: Naver Corporation, Naver Labs Corporation
    Inventors: Ginger Delmas, Philippe Weinzaepfel, Francesc Moreno-noguer, Gregory Rogez
  • Patent number: 12165401
    Abstract: 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: Grant
    Filed: March 13, 2023
    Date of Patent: December 10, 2024
    Assignee: NAVER CORPORATION
    Inventors: Philippe Weinzaepfel, Gregory Rogez
  • Patent number: 12164559
    Abstract: Am image retrieval system includes: a neural network (NN) module configured to generate local features based on an input image; an iterative attention module configured to, via T iterations, generate an ordered set of super features in the input image based on the local features, where T is an integer greater than 1; and a selection module configured to select a second image from a plurality of images in an image database based on the second image having a second ordered set of super features that most closely match the ordered set of super features in the input image, where the super features in the set of super features do not include redundant local features of the input image.
    Type: Grant
    Filed: January 21, 2022
    Date of Patent: December 10, 2024
    Assignee: NAVER CORPORATION
    Inventors: Philippe Weinzaepfel, Thomas Lucas, Diane Larlus, Ioannis Kalantidis
  • Publication number: 20240404104
    Abstract: A computer implemented method and system using an object-agnostic model for predicting a pose of an object in an image receives a query image having a target object therein; receives a set of reference images of the target object from different viewpoints; encodes, using a vision transformer, the received query image and the received set of reference images to generate a set of token features for the received query image and a set of token features for the received set of reference images; extracts, using a transformer decoder, information from the set of token features for the encoded reference images with respect to a set of token features for the received query image; processes, using a prediction head, the combined set of token features to generate a 2D-3D mapping and a confidence map of the query image; and processes the 2D-3D mapping and confidence map to determine the pose of the target object in the query image.
    Type: Application
    Filed: April 25, 2024
    Publication date: December 5, 2024
    Applicant: Naver Labs Corporation
    Inventors: Jérome Revaud, Romain Brégier, Yohann Cabon, Philippe Weinzaepfel, JongMin Lee
  • Patent number: 12013700
    Abstract: A training system includes: an encoder module configured to receive a query image and to generate a first vector representative of one or more features in the query image using an encoder; a mixing module configured to generate a second vector by mixing a third vector, representative of one or more features in a second image that is classified as a negative relative to the query image, with a fourth vector; and an adjustment module configured to train the encoder by selectively adjusting one or more parameters of the encoder based on the first vector and the second vector.
    Type: Grant
    Filed: April 23, 2021
    Date of Patent: June 18, 2024
    Assignee: NAVER CORPORATION
    Inventors: Ioannis Kalantidis, Diane Larlus, Philippe Weinzaepfel, Mert Bulent Sariyildiz, Noé Pion
  • Publication number: 20240144019
    Abstract: 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: Application
    Filed: August 29, 2023
    Publication date: May 2, 2024
    Applicants: NAVER CORPORATION, NAVER LABS CORPORATION
    Inventors: 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
  • Publication number: 20240135695
    Abstract: A method includes: performing unsupervised pre-training of a model, the model including and a decoder including: obtaining a first image and a second image under different conditions or from different viewpoints; encoding, by the encoder, the first image into a representation of the first image and the second image into a representation of the second image; transforming the representation of the first image into a transformed representation; decoding, by the decoder, the transformed representation into a reconstructed image, where the transforming of the representation of the first image and the decoding of the transformed representation is based on the representation of the first image and the representation of the second image; and adjusting one or more parameters of at least one of the encoder and the decoder based on minimizing a loss; and fine-tuning the model, initialized with a set of task specific encoder parameters, for a geometric vision task.
    Type: Application
    Filed: August 4, 2023
    Publication date: April 25, 2024
    Applicants: NAVER CORPORATION, NAVER LABS CORPORATION
    Inventors: Romain BRÉGIER, Yohann CABON, Thomas LUCAS, Jérôme REVAUD, Philippe WEINZAEPFEL, Boris CHIDLOVSKII, Vincent LEROY, Leonid ANTSFELD, Gabriela CSURKA KHEDARI
  • Publication number: 20240127462
    Abstract: 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: Application
    Filed: September 29, 2022
    Publication date: April 18, 2024
    Applicants: NAVER CORPORATION, NAVER LABS CORPORATION
    Inventors: Thomas LUCAS, Fabien BARADEL, Philippe WEINZAEPFEL, Gregory ROGEZ
  • Publication number: 20240051125
    Abstract: A system includes: a hand module to, based on a demonstration of a human hand grasping an object, determine first and second vectors that are normal to and parallel to a palm of the human hand, respectively, and a position of the human hand; a gripper module to determine third and fourth vectors that are normal to and parallel to a palm of a gripper of a robot, respectively, and a present position of the gripper; and an actuation module to: move the gripper when open such that the present position of the gripper is at the position of the human hand, the third and first vectors are aligned, and the fourth and second vectors are aligned; close fingers of the gripper based on minimizing a first loss; and actuate the fingers of the gripper to minimize a second loss determined based on the first loss and a third loss.
    Type: Application
    Filed: February 22, 2023
    Publication date: February 15, 2024
    Applicants: Naver Corporation, Naver Labs Corporation, Ecole Nationale des Ponts et Chaussées
    Inventors: Yuming DU, Romain BREGIER, Philippe WEINZAEPFEL, Vincent LEPETIT
  • Publication number: 20230306718
    Abstract: A computer-implemented method includes: obtaining a pair of images depicting a same scene, the pair of images including a first image with a first pixel grid and a second image with a second pixel grid, the first pixel grid different than the second pixel grid; by a neural network module having a first set of parameters: generating a first feature map based on the first image; and generating a second feature map based on the second image; determining a first correlation volume based on the first and second feature maps; iteratively determining a second correlation volume based on the first correlation volume; determining a loss for the first and second feature maps based on the second correlation volume; generating a second set of the parameters based on minimizing a loss function using the loss; and updating the neural network module to include the second set of parameters.
    Type: Application
    Filed: January 30, 2023
    Publication date: September 28, 2023
    Applicant: NAVER CORPORATION
    Inventors: Jérome REVAUD, Vincent LEROY, Philippe WEINZAEPFEL, Boris CHIDLOVSKII
  • Publication number: 20230215160
    Abstract: 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: Application
    Filed: March 13, 2023
    Publication date: July 6, 2023
    Applicant: Naver Corporation
    Inventors: Philippe WEINZAEPFEL, Gregory Rogez
  • Patent number: 11651608
    Abstract: 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: Grant
    Filed: September 13, 2022
    Date of Patent: May 16, 2023
    Assignees: NAVER CORPORATION, NAVER LABS CORPORATION
    Inventors: Philippe Weinzaepfel, Romain Bregier, Hadrien Combaluzier, Vincent Leroy, Gregory Rogez
  • Publication number: 20230119559
    Abstract: 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: Application
    Filed: October 4, 2021
    Publication date: April 20, 2023
    Applicants: NAVER CORPORATION, NAVER LABS CORPORATION
    Inventors: Fabien BARADEL, Romain BREGIER, Thibault GROUEIX, Ioannis KALANTIDIS, Philippe WEINZAEPFEL, Gregory ROGEZ
  • Patent number: 11625953
    Abstract: 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: Grant
    Filed: June 17, 2020
    Date of Patent: April 11, 2023
    Assignee: NAVER CORPORATION
    Inventors: Philippe Weinzaepfel, Gregory Rogez
  • Publication number: 20230107921
    Abstract: Am image retrieval system includes: a neural network (NN) module configured to generate local features based on an input image; an iterative attention module configured to, via T iterations, generate an ordered set of super features in the input image based on the local features, where T is an integer greater than 1; and a selection module configured to select a second image from a plurality of images in an image database based on the second image having a second ordered set of super features that most closely match the ordered set of super features in the input image, where the super features in the set of super features do not include redundant local features of the input image.
    Type: Application
    Filed: January 21, 2022
    Publication date: April 6, 2023
    Applicants: NAVER CORPORATION, NAVER LABS CORPORATION
    Inventors: Philippe Weinzaepfel, Thomas Lucas, Diane Larlus, loannis Kalantidis
  • Publication number: 20230053716
    Abstract: 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: Application
    Filed: March 30, 2022
    Publication date: February 23, 2023
    Applicant: Naver Corporation
    Inventors: Philippe Weinzaepfel, Gregory Rogez, Thomas Lucas
  • Publication number: 20230015984
    Abstract: 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: Application
    Filed: September 13, 2022
    Publication date: January 19, 2023
    Applicants: NAVER CORPORATION, NAVER LABS CORPORATION
    Inventors: Philippe WEINZAEPFEL, Romain Bregier, Hadrien Combaluzier, Vincent Leroy, Gregory Rogez
  • Patent number: 11494932
    Abstract: 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: Grant
    Filed: June 2, 2020
    Date of Patent: November 8, 2022
    Assignees: NAVER CORPORATION, NAVER LABS CORPORATION
    Inventors: Philippe Weinzaepfel, Romain Bregier, Hadrien Combaluzier, Vincent Leroy, Gregory Rogez
  • Publication number: 20220114444
    Abstract: A computer-implemented method for training a neural network to perform a data processing task includes: for each data sample of a set of labeled data samples: by a first loss function for the data processing task, computing a first loss for that data sample; and by a second loss function, automatically computing a weight value for the data sample based on the first loss, the weight value indicative of a reliability of a label of the data sample predicted by the neural network for the data sample and dictating the extent to which that data sample impacts training of the neural network; and training the neural network with the set of labelled data samples according to their respective weight value.
    Type: Application
    Filed: July 23, 2021
    Publication date: April 14, 2022
    Applicant: NAVER CORPORATION
    Inventors: Philippe WEINZAEPFEL, Jérome REVAUD, Thibault CASTELLS