Abstract: A method with object detection includes obtaining a first point cloud feature based on point cloud data of an image and determining at least one object in the image based on the first point cloud feature.

Abstract: A three-dimensional (3D) rendering method includes extracting sample points from a 3D scene, acquiring rendering result information for the sample points by rendering the sample points, and generating a rendering result image corresponding to an entire rendering based on the rendering result information for the sample points and feature information of the 3D scene.

Abstract: A processor-implemented method includes: determining albedo data in a canonical space and depth data in the canonical space based on input image data including an object, using one or more neural network-based extraction models; generating deformed albedo data and deformed depth data by applying a target shape deformation value respectively to the albedo data and the depth data; generating resultant shaded data by performing shading based on the deformed depth data and a target illumination value; generating intermediate image data based on the resultant shaded data and the deformed albedo data; and generating reconstructed image data from the intermediate image data and the deformed depth data based on a target pose value.

Abstract: A processor-implemented method with object pose estimation includes: determining an image feature corresponding to a point cloud of an input image; determining semantic segmentation information, instance mask information, and keypoint information of an object, based on the image feature; and estimating a pose of the object based on the semantic segmentation information, the instance mask information, and the keypoint information.

Abstract: A processor-implemented neural network operating method, the operating method comprising obtaining a neural network pre-trained in a source domain and a first style feature of the source domain, extracting a second style feature of a target domain from received input data of the target domain, using the neural network, performing domain adaptation of the input data, by performing style matching of the input data based on the first style feature of the source domain and the second style feature of the target domain, and processing the style-matched input data, using the neural network.

Abstract: A method, an apparatus, an electronic device for estimating a pose of an object include determining a confidence of a depth image of an object based on a color image and the depth image of the object, estimating a pose of the object based on a three-dimensional (3D) keypoint in response to the depth image being reliable, and estimating the pose of the object based on a two-dimensional (2D) keypoint in response to the depth image being unreliable.

Abstract: An image processing method is provided. The method includes generating appearance information corresponding to a canonical space defined by a cube including a plurality of regular grids in a first input image by implementing a first neural network, generating geometric information corresponding to a standard space in a second input image by implementing a second neural network, deforming the plurality of regular grids based on the generated geometric information, generating standardized image information by applying the appearance information to the plurality of deformed regular grids, and generating an output image based on the standardized image information.

Abstract: A processor-implemented method includes training a first model to predict confidences of labels for data samples in a training dataset, including using a corrected data sample obtained by correcting an incorrect label based on a corresponding confidence detected by the first model and an estimated corrected label generated by a second model; training the second model to estimate correct labels for the data samples, including estimating a correct other label corresponding to another incorrect label detected based on a corresponding confidence generated by the first model with respect to the other incorrect label; and automatically correcting the other incorrect label with the estimated correct other label.

Abstract: A processor-implemented method includes adjusting a virtual content object based on a shape of the virtual content object projected onto a projection plane; and visualizing the adjusted virtual content object on the projection plane.

Abstract: A method and apparatus with pose determination are provided. The method includes determining first pose information of a computing apparatus dependent on motion sensor information of motion of the computing apparatus, estimating second pose information of the computing apparatus dependent on feature point position information that is pre-defined for an object, and one or more feature points of the object that are extracted from an image captured by the computing apparatus, determining respective reliability values of the first pose information and the second pose information, and determining a pose of the computing apparatus for augmented reality (AR) content based on the first pose information, the second pose information, and the respective reliability values. AR content may be generated based on the determined pose.

Abstract: A stereo matching method includes obtaining a first feature map associated with a first view image and a second feature map associated with a second view image using a neural network model-based feature extractor, determining respective matching costs between a reference pixel of the first view image and candidate pixels of the second view image using the first feature map and the second feature map, and determining a pixel corresponding to the reference pixel among the candidate pixels based on the determined matching costs.

Abstract: A method and apparatus for generating an image and for training an artificial neural network to generate an image are provided. The method of generating an image, including receiving input data comprising conditional information and image information, generating a synthesized image by applying the input data to an image generation neural network configured to maintain geometric information of the image information and to transform the remaining image information based on the conditional information, and outputting the synthesized image.

Abstract: A method and apparatus with feature embedding is provided. The method includes estimating a depth map for each of plural two-dimensional (2D) input images, transforming the depth maps into three-dimensional (3D) information in a point cloud form based on an aggregation of the depth maps, and generating an embedded feature by applying the 3D information to a machine learning model, where the embedded feature includes information about a 3D shape corresponding to a 2D object in the plural 2D input images.

Abstract: A processor-implemented method includes: adjusting a virtual content object based on a shape of the virtual content object projected onto a projection plane; and visualizing the adjusted virtual content object on the projection plane.

Abstract: Disclosed is a localization method and apparatus that may acquire localization information of a device, generate a first image that includes a directional characteristic corresponding to an object included in an input image, generate a second image in which the object is projected based on the localization information, to map data corresponding to a location of the object, and adjust the localization information based on visual alignment between the first image and the second image.

Abstract: An image processing method is provided. The method includes generating appearance information corresponding to a canonical space defined by a cube including a plurality of regular grids in a first input image by implementing a first neural network, generating geometric information corresponding to a standard space in a second input image by implementing a second neural network, deforming the plurality of regular grids based on the generated geometric information, generating standardized image information by applying the appearance information to the plurality of deformed regular grids, and generating an output image based on the standardized image information.

Abstract: A method includes generating a first rendering based on input elements of an input scene, generating a second rendering by inputting a result of the first rendering to a generative machine learning model that is based on an artificial neural network (ANN), and generating a rendered output image based on the result of the first rendering and a result of the second rendering.

Abstract: A processor-implemented neural network operating method, the operating method comprising obtaining a neural network pre-trained in a source domain and a first style feature of the source domain, extracting a second style feature of a target domain from received input data of the target domain, using the neural network, performing domain adaptation of the input data, by performing style matching of the input data based on the first style feature of the source domain and the second style feature of the target domain, and processing the style-matched input data, using the neural network.

Abstract: An apparatus and method of estimating an object posture are provided. A method includes determining key point information in an image to be processed, determining modified key point information based on a key point feature map corresponding to the key point information, and estimating an object posture of an object in the image to be processed, based on the modified key point information.

Abstract: An electronic device and method with gaze estimating are disclosed. The method includes obtaining target information of an image, the image including an eye, obtaining a target feature map representing information on the eye in the image based on the target information, and estimating a gaze for the eye in the image based on the target feature map. The target information includes either attention information on the image, or a distance between pixels in the image, or both.