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: 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.

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: A processor-implement method with image processing includes: generating a feature map of a first image and detecting a target region in the first image based on the feature map; correcting the detected target region; and processing an object corresponding to the target region, based on the corrected target region.

Abstract: A processor-implemented method with pose estimation includes: determining depth data by sensing a depth of a target scene comprising planes orthogonal to each other; determining normal vectors corresponding to depth points of the depth data; determining orientation data of a device by comparing the normal vectors to orientation candidates; and determining position data of the device based on distances from the device to the planes.

Abstract: A method, apparatus, electronic device, and non-transitory computer-readable storage medium with multi-modal feature fusion are provided. The method includes generating three-dimensional (3D) feature information and two-dimensional (2D) feature information based on a color image and a depth image, generating fused feature information by fusing the 3D feature information and the 2D feature information based on an attention mechanism, and generating predicted image information by performing image processing based on the fused feature information.

Abstract: A method of estimating a position includes: determining a first rotation reference point based on localization information estimated for a target and map data; estimating a second rotation reference point from image data associated with a front view of the target; and correcting the localization information using a rotation parameter calculated based on the first rotation reference point and the second rotation reference point.

Abstract: A processor-implemented method using a neural network (NN) includes: receiving input data; and determining information inferred from the input data based on state information about a state in which the NN is activated in response to the input data, wherein an embedding vector generated by encoding the input data using at least a portion of the NN comprises information used to reconstruct a first partial region of the input data with a first accuracy and to reconstruct a second partial region of the input data with a second accuracy, and wherein the first partial region is adaptively determined based on either one or both of the inferred information and the embedding vector.

Abstract: Provided is a method and apparatus for detecting a planar surface, the method including acquiring, based on a pixelwise disparity of an input image estimated in a first network, a pixelwise plane parameter of the input image, determining a pixelwise segment matching probability of the input image based on a second network trained to perform a segmentation of an image, acquiring a segment-wise plane parameter based on the pixelwise plane parameter and the pixelwise segment matching probability, and detecting a planar surface in the input image based on the segment-wise plane parameter.

Abstract: Disclosed is a recognition and training method and apparatus. The apparatus may include a processor configured to input data to a neural network, determine corresponding to a multiclass output a mapping function of a first class and a mapping function of a second class, acquire a result of a loss function including a first probability component that changes correspondingly to a function value of the mapping function of the first class and a second probability component that changes contrastingly to a function value of the mapping function of the second class, determine a gradient of loss corresponding to the input data based on the result of the loss function, update a parameter of the neural network based on the determined gradient of loss for generating a trained neural network based on the updated parameter. The apparatus may input other data to the trained neural network, and indicate a recognition result.

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.