In one implementation, a method includes: displaying a UI element as an overlay in a UI associated with a first FOV, wherein the first FOV is characterized by a first viewing vector of a physical environment; detecting a change from the first FOV to a second FOV, wherein the second FOV is characterized by a second viewing vector of the physical environment; and in response to detecting the change from the first FOV to the second FOV, determining a prominence-display value for the UI element; if the prominence-display value for the UI element exceeds a prominence threshold, displaying the UI element as the overlay in the UI associated with the second FOV; and if the prominence-display value for the UI element does not exceed the prominence threshold, ceasing display of the UI element in the UI associated with the second FOV.

An image generation device includes: at least one memory storing a set of instructions; and at least one processor configured to execute the set of instructions to: select a second face image from a plurality of face images stored in advance based on directions of faces included in the plurality of face images and a direction of a face included in an input first face image; deform the second face image based on feature points of the face included in the first face image and feature points of a face included in the second face image such that a face region of the second face image matches a face region of the first face image; and generate a third face image in which the face region of the first face image is synthesized with a region other than the face region of the deformed second face image.

Methods, systems, and articles of manufacture to improve image recognition searching are disclosed. In some embodiments, a first document image of a known object is used to generate one or more other document images of the same object by applying one or more techniques for synthetically generating images. The synthetically generated images correspond to different variations in conditions under which a potential query image might be captured. Extracted features from an initial image of a known object and features extracted from the one or more synthetically generated images are stored, along with their locations, as part of a common model of the known object. In other embodiments, image recognition search effectiveness is improved by transforming the location of features of multiple images of a same known object into a common coordinate system. This can enhance the accuracy of certain aspects of existing image search/recognition techniques including, for example, geometric verification.

The training apparatus (2000) performs a first phase training and a second phase training of a discriminator (10). The discriminator (10) acquires a ground-view image and an aerial-view image, and determines whether the acquired ground-view image matches the acquired aerial-view image. The first phase training is performed using a ground-view image and a first level negative example of aerial-view image. The first level negative example of aerial-view image includes scenery of a different type from scenery in the ground-view image. The second phase training is performed using the ground-view image and a second level negative example of aerial-view image. The second level negative example of aerial-view image includes scenery of a same type as scenery in the ground-view image.

Various disclosed embodiments are directed to inpainting one or more portions of a target image based on merging (or selecting) one or more portions of a warped image with (or from) one or more portions of an inpainting candidate (e.g., via a learning model). This, among other functionality described herein, resolves the inaccuracies of existing image inpainting technologies.

Provided are systems, methods, and devices for generating digital and/or cryptographic assets. An initial state of an environment is acquired using sensors that includes a state of each sensor, a region of interest including a 3D body, and a state of light sources. The asset is associated with the 3D body. A plurality of boundary conditions associated with a workflow for capturing the asset is determined. A visualization of a set of boundary conditions is displayed on a display that includes a plurality of visual cues including first and second visual cues. Each respective visual cue provides a visual indication of a state of a corresponding boundary condition in the set of boundary conditions. At least one visual cue is updated when each boundary condition in the set of boundary conditions is satisfied. When satisfied, the workflow at the computer-enabled imaging device is executed, thereby capturing the asset.

Aspects relate to providing a counterfactual inference management technique capable of providing increased flexibility to allow users to select an appropriate counterfactual inference and offering scalability for handling tabular data and image data in a single configuration. A counterfactual inference management device comprising a classifier unit trained to determine whether a set of input data that includes a set of data features achieves a predetermined target and a counterfactual inference unit for generating a set of transformed data in which a subset of the set of data features are modified to counterfactual features. The classifier unit processes the set of transformed data to determine whether it achieves the predetermined target and calculates a counterfactual loss. The counterfactual inference unit is trained to reduce the counterfactual loss and generate a set of transformed data including counterfactual features that achieve the predetermined target.

Disclosed herein is a method for blending warped images based on depth distribution. The method includes generating images warped to a virtual viewpoint using input images, generating a blended warped image based on the warped images, and generating a final virtual viewpoint image by applying inpainting to the blended warped image.

A method includes generating a first warped image based on a pose and a depth estimated from a current image and a previous image in a sequence of images captured by a camera of the agent. The method also includes estimating a motion of dynamic object between the previous image and the target image. The method further includes generating a second warped image from the first warped image based on the estimated motion. The method still further includes controlling an action of an agent based on the second warped image.

A system for creating synthetic data for testing an autonomous system, comprising at least one hardware processor adapted to execute a code for: using a machine learning model to compute a plurality of depth maps based on a plurality of real signals captured simultaneously from a common physical scene, each of the plurality of real signals are captured by one of a plurality of sensors, each of the plurality of computed depth maps qualifies one of the plurality of real signals; applying a point of view transformation to the plurality of real signals and the plurality of depth maps, to produce synthetic data simulating a possible signal captured from the common physical scene by a target sensor in an identified position relative to the plurality of sensors; and providing the synthetic data to at least one testing engine to test an autonomous system comprising the target sensor.