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 method and apparatus for generating a virtual object are provided, the method includes acquiring a point cloud of an object to generate a virtual object, determining shape attribute information of the object based on an image of the object, changing a position of at least one point in the point cloud based on the shape attribute information, and generating a virtual object for the object based on a changed point cloud.

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 includes: calculating edge direction information of edge component pixels extracted from an input image; determining an inlier pixel among the edge component pixels based on the edge direction information and virtual horizontal line information; estimating a rotation reference point based on the inlier pixel; and correcting localization information of the apparatus based on the estimated rotation reference point.

Abstract: A modeling method searches for a sequence matched to a user input using a fluid animation graph generated based on similarities among frames included in sequences included in the fluid animation graph and models a movement corresponding to the user input based on a result of the searching. Provided also is a corresponding apparatus and a method for preprocessing for such modeling.

Abstract: A method and apparatus for processing an image interaction are provided. The apparatus extracts, using an encoder, an input feature from an input image, converts the input feature to a second feature based on an interaction for an application to the input image, and generates, using a decoder, a result image from the second feature.

Abstract: A modeling method searches for a sequence matched to a user input using a fluid animation graph generated based on similarities among frames included in sequences included in the fluid animation graph and models a movement corresponding to the user input based on a result of the searching. Provided also is a corresponding apparatus and a method for preprocessing for such modeling.

Abstract: A method of modeling an object includes defining a state transition probability and a state of each of a plurality of particles forming the object; changing a state of a particle defined to be in a first state among the plurality of particles to a second state; applying a movement model to a particle defined to be in the second state among the plurality of particles; and changing a state of the particle defined to be in the second state to the first state based on the state transition probability.

Abstract: A method includes actuating a processor to apply an input image to a feature extractor including a plurality of layers, determine a third feature vector based on first feature vectors of an input image output by a first layer included in a feature extractor and second feature vectors of the input image output by a second layer in the feature extractor, and identify an object in the input image based on the third feature vector.

Abstract: Localization apparatuses and methods are disclosed where a localization apparatus extracts a feature of an object from an input image, generates an image in which the object is projected with respect to localization information of a device based on map data, and evaluates the localization information based on feature values corresponding to vertices included in a projection image.

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: Provided are method and apparatuses related to material recognition and training. A training apparatus for material recognition generates training data associated with a material by generating a texture image having a texture attribute from an object image and recognizing material information from the texture image using a material model.

Abstract: A modeling method based on particles, the method including generating coarse particles by down-sampling target particles corresponding to at least a portion of a target object, calculating a correcting value enabling the coarse particles to satisfy constraints of the target object based on physical attributes of the target particles, applying the correcting value to the target particles, and redefining the target particles in response to the target particles to which the correcting value is applied satisfying the constraints.

Abstract: A method to process an image includes: determining a first specular reflection element from an input image based on a feature of chromaticity of pixels included in the input image; extracting a second specular reflection element from the first specular reflection element based on a feature of brightness of the pixels; and correcting the input image based on the second specular reflection element.

Abstract: A method and device for adjusting a brightness of a display includes, determining current viewpoint brightness information of a current viewpoint region on the display corresponding to a user current viewed point or area of the display, determining previous brightness information of a previous viewpoint region of the display corresponding to a previously viewed point or area of the display, and controlling a displaying of a current image, including the current viewpoint region, with an adjusted brightness for a partial region of the display based on a comparison of the current viewpoint brightness information and the previous brightness information.

Abstract: A method of displaying an illumination includes: based on illumination information, determining, using a least one processor, an illumination area to which an illumination assigned in a virtual space is projected; and visualizing, using the at least one processor, an illumination effect produced by the illumination with respect to a determined boundary area in the illumination area, the determined boundary area including a boundary and a portion of the determined illumination area that is less than all of the determined illumination area.

Abstract: A method and apparatus for processing an image interaction are provided. The apparatus extracts, using an encoder, an input feature from an input image, converts the input feature to a second feature based on an interaction for an application to the input image, and generates, using a decoder, a result image from the second feature.

Abstract: A method and apparatus for generating a virtual object are provided, the method includes acquiring a point cloud of an object to generate a virtual object, determining shape attribute information of the object based on an image of the object, changing a position of at least one point in the point cloud based on the shape attribute information, and generating a virtual object for the object based on a changed point cloud.

Abstract: A method and apparatus for modeling smoke turbulence is disclosed. The method of modeling smoke turbulence includes detecting a turbulent area comprising smoke turbulence in a low-resolution image, and searching for a high-resolution patch matching the turbulent area in a memory that comprises high-resolution patches representing smoke turbulence extracted from high-resolution images. The method also models the smoke turbulence by synthesizing the retrieved patch to the low-resolution image.

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.