Abstract: Provided are methods and apparatuses for determining a target object in an image based on an interactive input. A target object determining method acquires first feature information corresponding to an image and second feature information corresponding to an interactive input; and determines a target object corresponding to the interactive input from among objects in the image based on the first feature information and the second feature information.

Abstract: A method with image augmentation includes recognizing, based on a gaze of the user corresponding to the input image, any one or any combination of any two or more of an object of interest of a user, a situation of the object of interest, and a task of the user from partial regions of an input image determining relevant information indicating an intention of the user, based on any two or any other combination of the object of interest of the user, the situation of the object of interest, and the task of the user, and generating a visually augmented image by visually augmenting the input image based on the relevant information.

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 with image restoration includes: receiving an input image and a first task vector indicating a first image effect among candidate image effects; extracting a common feature shared by the candidate image effects from the input image, based on a task-agnostic architecture of a source neural network; and restoring the common feature to a first restoration image corresponding to the first image effect, based on a task-specific architecture of the source neural network and the first task vector.

Abstract: An object detection method includes setting a first window region and a second window region larger than the first window region that correspond to partial regions of different sizes in an input image, downsampling the second window region to generate a resized second window region, detecting a first object candidate from the first window region and a second object candidate from the resized second window region, and detecting an object included in the input image based on one or both of the first object candidate and the second object candidate.

Abstract: An image processing apparatus includes a first processor configured to obtain, from a color image, an illumination element image and an albedo element image corresponding to the color image, and a second processor configured to divide the illumination element image into a plurality of subelement images each corresponding to the color image.

Abstract: A method and apparatus for predicting an object of interest of a user receives an input image of a visible region of a user and gaze information including a gaze sequence of the user, generates weight filters for a per-frame segmentation image by analyzing a frame of the input image for input characteristics of the per-frame segmentation image and the gaze information, and predicts an object of interest of the user by integrating the weight filters and applies the integrated weight filter to the per-frame segmentation image.

Abstract: A method with image augmentation includes recognizing, based on a gaze of the user corresponding to the input image, any one or any combination of any two or more of an object of interest of a user, a situation of the object of interest, and a task of the user from partial regions of an input image determining relevant information indicating an intention of the user, based on any two or any other combination of the object of interest of the user, the situation of the object of interest, and the task of the user, and generating a visually augmented image by visually augmenting the input image based on the relevant information.

Abstract: An image acquisition apparatus includes a display layer including hole regions through which external light is received and pixel regions arranged between the hole regions, an image sensor disposed under the display layer and configured to generate a raw image by sensing the external light received through the hole regions, and a processor configured to perform image processing on the raw image based on blur information based on an arrangement of the hole regions.

Abstract: A method and apparatus for controlling an augmented reality (AR) apparatus are provided. The method includes acquiring a video, detecting a human body from the acquired video, performing an action prediction with regard to the detected human body, and controlling the AR apparatus based on a result of the action prediction and a mapping relationship between human body actions and AR functions.

Abstract: Provided is a method and apparatus for aligning a three-dimensional (3D) model. The 3D model alignment method includes acquiring, by a processor, at least one two-dimensional (2D) image including an object, detecting, by the processor, a feature point of the object in the at least one 2D input image using a neural network, estimating, by the processor, a 3D pose of the object in the at least one 2D input image using the neural network, retrieving, by the processor, a target 3D model based on the estimated 3D pose, and aligning, by the processor, the target 3D model and the object based on the feature point.

Abstract: An image acquisition apparatus includes a display layer including hole regions through which external light is received and pixel regions arranged between the hole regions, an image sensor disposed under the display layer and configured to generate a raw image by sensing the external light received through the hole regions, and a processor configured to perform image processing on the raw image based on blur information based on an arrangement of the hole regions.

Abstract: An image enhancement method, an image enhancement apparatus, and a method and apparatus for training the image enhancement apparatus are provided. The image enhancement apparatus may enhance a quality of a raw image captured by an under-display camera (UDC). The image enhancement apparatus may process the raw image for each channel according to characteristics of a photographing environment of the UDC. The image enhancement apparatus may further enhance the image by processing the raw image to reflect characteristics for each channel.

Abstract: An image acquisition apparatus includes a display layer including hole regions through which external light is received and pixel regions arranged between the hole regions, an image sensor disposed under the display layer and configured to generate a raw image by sensing the external light received through the hole regions, and a processor configured to perform image processing on the raw image based on blur information based on an arrangement of the hole regions.

Abstract: A method and apparatus for predicting an intention acquires a gaze sequence of a user, acquires an input image corresponding to the gaze sequence, generates a coded image by visually encoding temporal information included in the gaze sequence to the input image, and predicts an intention of the user corresponding to the gaze sequence based on the input image and the coded image.

Abstract: A display method includes displaying, in a virtual environment, an object to which a light source is set. The method further includes illuminating an area around the object based on the light source.

Abstract: Disclosed is a depth map calculation method and apparatus. The depth map calculation method includes calculating a global sparse depth map corresponding to a current frame using frames including the current frame, calculating a local dense depth map corresponding to the current frame using the current frame, extracting a non-static object region from the current frame by masking a static object region, removing the non-static object region from the global sparse depth map, and generating a global dense depth map corresponding to the current frame by merging the non-static object region-removed global sparse depth map and the local dense depth map.

Abstract: A method and apparatus for predicting an object of interest of a user receives an input image of a visible region of a user and gaze information including a gaze sequence of the user, generates weight filters for a per-frame segmentation image by analyzing a frame of the input image for input characteristics of the per-frame segmentation image and the gaze information, and predicts an object of interest of the user by integrating the weight filters and applies the integrated weight filter to the per-frame segmentation image.

Abstract: A method of refining a depth image includes extracting shading information of color pixels from a color image, and refining a depth image corresponding to the color image based on surface normal information of an object included in the shading information.

Abstract: A pose estimation method and apparatus is disclosed.