Abstract: An image processing apparatus includes a storage device storing coding information indicating light transmission characteristics of an encoding mask including optical filters that are arranged in two dimensions and whose light transmission characteristics are different from one another and a signal processing circuit that generates reconstructed images on a basis of a compressed image generated through imaging that employs the encoding mask and the coding information, that estimates an error in the reconstructed images, and that outputs a signal indicating the error.

Abstract: This application provides a point cloud decoding performed by an electronic device. The method includes: obtaining a current point to be decoded from a point cloud code stream, the current point comprising N types of attribute information, and N being an integer greater than 1; determining M attribute groups of the N types of attribute information of the current point, M being a positive integer less than N; and perform uniform decoding for each attribute information in a same attribute group of the current point using a first operation, the first operation comprising at least one of a neighbor point determination and an attribute predicted value calculation. According to this application, the point cloud decoding method avoids the repeated searching, reduces time complexity during attribute decoding and improves attribute decoding efficiency.

Abstract: An encoding method includes following operations. Neighbouring points of a current point in a point cloud to be encoded are determined, and a first parameter is calculated according to the neighbouring points. The first parameter is a difference between a maximum value and a minimum value among reconstructed values of first attributes of the neighbouring points. In response to the first parameter being less than a threshold, a prediction value of the first attribute of the current point is determined by using a preset first prediction mode. A difference between an original value of the first attribute of the current point and the prediction value is calculated as a residual value of the first attribute of the current point. The residual value subjected to quantization is encoded. Identification information of the first prediction mode is signalled, where the identification information is used for indicating a prediction mode.

Abstract: A point cloud encoding method is provided. The method includes the following. A residual value of attribute information of a current point in a point cloud is determined according to a prediction value of the attribute information of the current point. An index of the current point is determined. Lossless encoding is performed on the residual value of the attribute information of the current point, when the index of the current point belongs to an index range of a first point. A quantized residual value of the attribute information of the current point is obtained by quantizing the residual value of the attribute information of the current point based on a quantization weight of the current point and a quantization step size of the current point, when the index of the current point is beyond the index range of the first point, and the quantized residual value is encoded.

Abstract: This application provides methods and apparatuses for processing of picture data or picture feature data using a neural network with two or more layers. The present disclosure may be applied in the field of artificial intelligence (AI)-based video or picture compression technologies, and in particular, to the field of neural network-based video compression technologies. According to some embodiments, position within the neural network, at which auxiliary information may be entered for processing is selectable based on a gathering condition. The gathering condition may assess whether some prerequisite is fulfilled. Some of the advantages may include better performance in terms of rate and/or disclosure due to the effect of increased flexibility in neural network configurability.

Abstract: Methods and systems for compressing shape data for a set of electronic designs include inputting a set of shape data, where the shape data comprises mask designs. A convolutional autoencoder encodes the set of shape data, where the encoding compresses the set of shape data to produce a set of encoded shape data. The convolutional autoencoder is tuned for increased accuracy of the set of encoded shape data based on design rules for the set of electronic designs. The convolutional autoencoder comprises a set of parameters comprising weights, and the convolutional autoencoder has been trained to retain important information needed, based on the design rules for the set of electronic designs.

Abstract: A point cloud encoder and decoder are configured to code point cloud data using predictive geometry coding and a vertical predictor. A vertical predictor is a previously coded point in the point cloud having a different laser ID compared to the currently coded point. The point cloud encoder and decoder may be configured to determine a pivot laser ID, determine a vertical predictor for a current point of the point cloud data, wherein the vertical predictor is based on a second point having a second laser ID different than the pivot laser ID, and code the current point using the vertical predictor and predictive geometry decoding.

Abstract: Systems and methods for facial image generation are described. One aspect of the systems and methods includes receiving an image depicting a face, wherein the face has an identity non-related attribute and a first identity-related attribute; encoding the image to obtain an identity non-related attribute vector in an identity non-related attribute vector space, wherein the identity non-related attribute vector represents the identity non-related attribute; selecting an identity-related vector from an identity-related vector space, wherein the identity-related vector represents a second identity-related attribute different from the first identity-related attribute; generating a modified latent vector in a latent vector space based on the identity non-related attribute vector and the identity-related vector; and generating a modified image based on the modified latent vector, wherein the modified image depicts a face that has the identity non-related attribute and the second identity-related attribute.

Abstract: One or more computing devices are configured to receiving image data from a camera on a harvester during a harvest operation, apply a machine learning model to identify, using only image data from the camera, a receiving vehicle from among a plurality of possible different receiving vehicles, determine dimensions associated with the identified receiving vehicle, and determine, using the dimensions and the image data, a location of the receiving vehicle relative to the harvester. An electronic device including a graphical user interface is configured to receive the location data from the one or more computing devices, use the location data to generate a graphical representation illustrating the relative positions of an unload conveyor of the harvester and a grain bin of the receiving vehicle, and present the graphical representation on the graphical user interface.

Abstract: The image transmission system executes the following five steps. The first step is acquiring a first image by capturing a first person present in a first area by a first camera. The second step is generating a first person silhouette image representing a silhouette of the first person shown in the first image. The third step is projecting or displaying the first person silhouette image on a second object installed in a second area that is different from the first area. The fourth step is determining whether or not the first person performs a first trigger action based on the first image. Fifth step is when the first person performs the first trigger action, projecting or displaying a first person detailed image on the second object, wherein the first person detailed image is closer to a real image of the first person than the first person silhouette image is.

Abstract: The present disclosure relates to system architectures for real-time processing and displaying of medical imaging data with no or reduced latency. An exemplary system for generating a video of a surgery can comprise: a processor configured to execute a plurality of instructions for a machine-learning model stored on a memory and a programmable circuit communicatively coupled with the processor. The programmable circuit is programmed to: receive surgical video data comprising a current frame; provide data related to the current frame to the processor; receive overlay data generated by the machine-learning model based on a previous frame captured before the current frame; generate, by a mixer of the programmable circuit, a composite frame based on the current frame and the overlay data; and output the composite frame for display as a part of the video of the surgery.

Abstract: Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and method for providing augmented reality-based makeup. The program and method provide for receiving a request to present augmented reality content in association with an image captured by a device camera, the image depicting a user's face; accessing an augmented reality content item applying makeup to the face, the augmented reality content configured to generate a mesh for tracking plural regions of the face and to present available makeup products with respect to the plural regions; presenting the augmented reality content item in association with the face depicted in the image; receiving user input selecting a region of the plural regions; determining a set of available makeup products corresponding to the selected region; and updating presentation of the augmented reality content item based on the set of available makeup products.

Abstract: A data processing system implements a receiving a textual input comprising a query for a first image. The data processing system also implements analyzing the textual input to determine a predicted color palette associated with a subject matter of the query; and procedurally generating the first image using the predicted color palette. Another implementation of the data processing system implements providing the textual input to a first machine learning model to obtain the first image, the first machine learning model being trained using a dataset comprising abstract imagery and analyzing the textual input using the first machine learning model to obtain the first image in response to receiving the textual input.

Abstract: Systems and methods for providing color recommendations for content editing applications include receiving a color recommendation request from a content editing application that includes color information pertaining to a document being edited by the content editing application. At least one color is identified based on at least one of a predefined accessibility standard, predetermined telemetry data, and organizational/group preferences with reference to the color information in the color recommendation request using a color recommendation service. A color recommendation indicating the at least one identified color is provided from the color recommendation service to the content editing application.

Abstract: Techniques are described for guiding stable diffusion to produce images with a contrasting foreground/background. Po stprocessing is implemented using segmentation and chromakeying to remove the background. These techniques extract an alpha channel in generated images to force stable diffusion to generate output with a background in a specified color, which is then removed from the image in output post-processing. Present techniques leverage the img2img inpainting pipeline with a noise mask that covers the image edges, applying noise (and generating content) only in the center of the image, thereby forcing a strong background/foreground distinction.

Abstract: A computer-implemented method for motion compensation of medical imaging data includes estimating, via a processor, non-periodic motion of a myocardium of a heart due to respiration and/or other body movements throughout a positron emission tomography (PET) scan based on list-mode emission data acquired during the PET scan of the heart of a subject. The method also includes performing, via the processor, event-by-event motion-corrected list-mode reconstruction on the list-mode emission data to generate cardiac images with the non-periodic motion removed.

Abstract: A dynamic frame reconstruction apparatus and method for medical image processing is disclosed which reduces the computationally expensive reconstruction of images but which retains the accuracy of the image reconstruction. A convolutional neural network is used to cluster the dynamic data into groups of frames, each group sharing similar radiotracer distribution. In one embodiment, groups of frames that have similar reconstruction parameters are determined, and scatter and random estimations are computed once and shared among each of the frames in the same frame group.

Abstract: Systems and methods for recreating images from undersampled MRI image data includes capturing undersampled MRI data and enhancing it with multiple cascading stages, each including a data consistency block in parallel to a convolutional neural network (CNN). The data consistency block adjusts each input image by a sensitivity map and performs hard replacement of acquired lines in k-space into the image. The CNN estimates a regularizer term that attempts to minimize a difference between a true image and the output of the data consistency block. At each stage, the output of CNN and data consistency block are added to create a set of output images that feed into the next stage.

Abstract: A method of training an image decomposition model, a method of decomposing an image, an electronic device, and a storage medium are provided. The method of training the image decomposition model includes: acquiring a training set; inputting first and second training images into first and second adversarial neural networks respectively, so as to determine a first loss function value; inputting a third training image into the first and second adversarial neural networks respectively, so as to determine a second loss function value; determining a third loss function value according to a comparison result between an acquired fusion image and the third training image, where the fusion image is generated by fusing generated images of the first and second adversarial neural networks, and adjusting a parameter of the image decomposition model according to at least one of the first to third loss function values.

Abstract: For reconstruction in medical imaging, such as reconstruction in MR imaging, an iterative, hierarchal network for regularization may decrease computational complexity. To further maintain computational complexity while improving robustness, auxiliary information is used in the regularization and corresponding reconstruction. The auxiliary information is in put to the machine-learned network.

Abstract: An image processing method includes a reconstruction step of reconstructing a radiographic image of a subject by performing reconstruction processing on radiological data of the subject, a count number calculation step of calculating a count number in a subject area in the radiographic image, a standard deviation calculation step of calculating a noise standard deviation in the radiographic image from a relation between the count number in each of a plurality of pre-acquired function calculation radiographic images and the noise standard deviation, by substituting the count number in the subject area into a pre-acquired basic noise deviation function a function in which a value of the count number and a value of the noise standard deviation correspond to each other, and a noise reduction processing step of performing NLM filter processing on the radiographic image using the noise standard deviation calculated in the standard deviation calculation step.

Abstract: A method for representing a geology includes receiving one or more drawing strokes as part of a geological feature sketch, predicting, using a machine learning model, one or more predicted geological features based at least in part on the one or more drawing strokes before the sketch is complete, displaying the one or more predicted geological features, receiving a selection of one of the one or more predicted geological features, and generating an image representing the geology including a digital representation of the selected one of the one or more predicted geological features.

Abstract: Systems and methods are provided for use in mapping tillage in fields based in remote data. One example computer-implemented method includes accessing, by a computing device, an image of one or more fields, where the image includes multiple pixels and where each of the pixels includes a value for each of multiple bands. The method also includes deriving, by the computing device, at least one index value the image and generating a map of tillage for the one or more fields using a trained model and the at least one index value for each of the pixels of the image. The method further includes storing the map of tillage for the one or more fields in a memory and causing display of the map of tillage for the one or more fields at an output device.

Abstract: A learning system (10) comprises: a generation unit (110) configured to synthesize with an original image including a living body, information on a reflection component of light not derived from the original image to generate a synthetic image; and a learning unit (130) configured to perform learning for an estimation model (121) based on information on a specific portion of the living body estimated from the synthetic image by the estimation model and correct answer information showing a correct answer of the information on the specific portion in the original image. According to this learning system, it is possible to learn the estimation model appropriately.

Abstract: Some implementations are directed to editing a source image, where the source image is one generated based on processing a source natural language (NL) prompt using a Large-scale language-image (LLI) model. Those implementations edit the source image based on user interface input that indicates an edit to the source NL prompt, and optionally independent of any user interface input that specifies a mask in the source image and/or independent of any other user interface input. Some implementations of the present disclosure are additionally or alternatively directed to applying prompt-to-prompt editing techniques to editing a source image that is one generated based on a real image, and that approximates the real image.

Abstract: Aspects concern a distributed computing system for generating vector tiles of a selected map area including a memory unit configured to store map data and a task database, the map data including a representation of the selected map area with a first resolution and a first detail level and with a second resolution higher than the first resolution and a second detail level; two or more processing units, each of the two or more processing units configured to select a task included in the task database, to execute the selected task, and to provide data generated by the selected task to the memory unit for storage; wherein one of the two or more processing units is further configured to schedule the generation of vector tiles by determining tasks using a specific predefined directed acyclic task graph and to provide the determined task to the task database.

Abstract: Techniques for generating emotionally-aware digital content are disclosed. In one embodiment, a method is disclosed comprising obtaining audio input, obtaining a textual representation of the audio input; using the textual representation of the audio input to identify an emotion corresponding to the audio input; generating an emotionally-aware facial representation in accordance with the textual representation and the identified emotion; using the emotionally-aware facial representation to generate one or more images comprising at least one facial expression corresponding to the identified emotion; and providing digital content comprising the one or more images.

Abstract: A method for restoration of a kinetic event using video. The method includes obtaining video involving the kinetic event including a plurality of frames. An absolute location of a frame of the plurality of frames is determined. Movement of a feature point cloud corresponding to an object in motion in the plurality of frames is analyzed. A 3D object model with substantially similar dimensions to the object in motion is selected. The 3D object model is displayed emulating the analysed movement on the 3D map based on the absolute location.

Abstract: Systems and methods for image retargeting are provided. Image data may be acquired that includes motion capture data indicative of motion of a plurality of markers disposed on a surface of a first subject. Each of the markers may be associated with a respective location on the first subject. A plurality of blendshapes may be calculated for the motion capture data based on a configuration of the markers. An error function may be identified for the plurality of blendshapes, and it may be determined that the plurality of blendshapes can be used to retarget a second subject based on the error function. The plurality of blendshapes may then be applied to a second subject to generate a new animation.

Abstract: Embodiments are disclosed for using machine learning models to perform three-dimensional garment deformation due to character body motion with collision handling. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an input, the input including character body shape parameters and character body pose parameters defining a character body, and garment parameters. The disclosed systems and methods further comprise generating, by a first neural network, a first set of garment vertices defining deformations of a garment with the character body based on the input. The disclosed systems and methods further comprise determining, by a second neural network, that the first set of garment vertices includes a second set of garment vertices penetrating the character body. The disclosed systems and methods further comprise modifying, by a third neural network, each garment vertex in the second set of garment vertices to positions outside the character body.

Abstract: A terminal with two separate displays is provided. The primary display is controlled by a transaction interface for performing transactions. The secondary display is controlled by an animation interface for rendering animations on the secondary display. An agent provides state transitions in the transaction interface to the animation interface and the animation interface transitions the animations rendered on the secondary display based on state changes reported by the agent. The animations rendered on the secondary display provide guidance, positive, and negative feedback to a consumer performing a transaction on the terminal through the transaction interface.

Abstract: To compute an image of a dynamic 3D scene comprising a 3D object, a description of a deformation of the 3D object is received, the description comprising a cage of primitive 3D elements and associated animation data from a physics engine or an articulated object model. For a pixel of the image the method computes a ray from a virtual camera through the pixel into the cage animated according to the animation data and computes a plurality of samples on the ray. Each sample is a 3D position and view direction in one of the 3D elements. The method computes a transformation of the samples into a canonical cage. For each transformed sample, the method queries a learnt radiance field parameterization of the 3D scene to obtain a color value and an opacity value. A volume rendering method is applied to the color and opacity values producing a pixel value of the image.

Abstract: A method for generating a firework visual effect, an electronic device, and a non-transitory computer readable storage medium are disclosed. The method for generating the firework visual effect includes: determining a visual effect trajectory; according to the visual effect trajectory, generating a firework particle set and a trail particle set in a three-dimensional space used for generating the firework visual effect; rendering the firework particle set to obtain a firework particle primitive model set; rendering the trail particle set to obtain a trail particle primitive model set; and performing superposition processing on the firework particle primitive model set and the trail particle primitive model set to generate a firework visual effect, wherein at least some trail particle primitive models in the trail particle primitive model set are at least displayed for a preset time period.

Abstract: A system and method for monitoring performance of an industrial process includes an input port for receiving signals representative of one or more performance parameters generated by the industrial process, a user interface including a display and a controller that is operably coupled with the input port and the user interface. The controller is configured to repeatedly receive signals over time via the input port representative of the one or more performance parameters of the industrial process and to generate a plurality of snapshots, wherein each snapshot includes a graphical representation of the one or more performance parameters of the industrial process at a corresponding time. The controller is configured to generate an animatable heat map including two or more of the plurality of snapshots arranged temporally and to display the animatable heat map on the display.

Abstract: Provided is a metaverse system. The metaverse system for providing metaverse content virtualizes a target space as a digital twin but statistically applies attributes and action patterns of objects in the target space to virtual objects in a virtual space, thereby reproducing the environment, atmosphere, and the like of the target space without accurately copying the attributes and action patterns of objects.

Abstract: Methods, user equipment and apparatus for controlling VR image in a communication network. The VR image is represented by image data being associated to respective parts of the image. The VR image data is handled on the basis of identifications indicating different viewports for a user.

Abstract: The present disclosure provides systems and methods for molecular imaging. The systems and methods may obtain, using at least one 3D camera, image data of at least one movable detector of an ECT device. The systems and methods may determine, based on the image data of the at least one movable detector, an arrangement of the at least one insert detector in a coordinate system that relates to the ECT device. The systems and methods may also obtain image data of an object by performing, using the at least one movable detector, an ECT imaging on the object. The systems and methods may generate, based on the arrangement of the at least one movable detector and the image data of the object, an image of the object.

Abstract: There is provided an apparatus configured to operate as a shader core, the shader core configured to perform a complex rendering process comprising a rendering process and a machine learning process, the shader core comprising: one or more tile buffers configured to store data locally to the shader core, wherein during the rendering process, the one or more tile buffers are configured to store rendered fragment data relating to a tile; and during the machine learning process, the one or more tile buffers are configured to store an input feature map, kernel weights or an output feature map relating to the machine learning process.

Abstract: The present disclosure relates to image rendering methods apparatuses, and electronic devices. One example method includes obtaining, by a central processing unit (CPU), an image command stream used to render an image frame, comparing the image command stream with a previous image command stream, and, when a same drawing command exists in the two image command streams, instructing a graphics processing unit (GPU) to render the image frame based on a previously generated drawing target.

Abstract: Disclosed herein is a web-based videoconference system that allows for video avatars to navigate within a virtual environment. Various methods for efficient modeling, rendering, and shading are disclosed herein.

Abstract: Apparatuses, systems, and techniques to generate blue noise masks for real-time image rendering and enhancement. In at least one embodiment, a vector-valued noise mask is generated and applied to one or more images to generate one or more enhanced images for image processing (e.g., real-time image rendering). In at least one embodiment, the noise mask includes vector values per pixel and is able to handle the temporal domain (e.g., add time to the spatial domain) to improve image quality when rendering images over multiple frames.

Abstract: This application provides an image rendering method and apparatus, an electronic device, a computer-readable storage medium, and a computer program product. The method comprises the following steps: acquiring texture data of a virtual object; performing differential fitting processing on the texture data to obtain fitted texture data; baking the fitted texture data into a texture image to obtain a rendered texture image; and performing, based on the rendered texture image, image rendering processing corresponding to the virtual.

Abstract: Systems and techniques are provided for widening a hierarchical structure for ray tracing. For instance, a process can include obtaining a plurality of primitives of a scene object included in a first hierarchical acceleration data structure and determining one or more candidate hierarchical acceleration data structures each including the plurality of primitives. A cost metric can be determined for the one or more candidate hierarchical acceleration data structures and, based on the cost metric, a compressibility prediction associated with a candidate hierarchical acceleration data structure of the one or more candidate hierarchical acceleration data structures can be determined. An output hierarchical acceleration data structure can be generated based on the compressibility prediction.

Abstract: Enhanced techniques applicable to a ray tracing hardware accelerator for traversing a hierarchical acceleration structure are disclosed. For example, traversal efficiency is improved by combining programmable traversals based on ray operations with per-node static configurations that modify traversal behavior. The per-node static configurations enable creators of acceleration data structures to optimize for potential traversals without necessarily requiring detailed information about ray characteristics and ray operations used when traversing the acceleration structure. Moreover, by providing for selective exclusion of certain nodes using per-node static configurations, less memory is needed to express an acceleration structure that includes, for example, different geometric levels of details corresponding to a single object.

Abstract: Recurrent blurring may be used to render frames of a virtual environment, where the radius of a filter for a pixel is based on a number of successfully accumulated frames that correspond to that pixel. To account for rejections of accumulated samples for the pixel, ray-traced samples from a lower resolution version of a ray-traced render may be used to increase the effective sample count for the pixel. Parallax may be used to control the accumulation speed along with an angle between a view vector that corresponds to the pixel. A magnitude of one or more dimensions of a filter applied to the pixel may be based on an angle of a view vector that corresponds to the pixel to cause reflections to elongate along an axis under glancing angles. The dimension(s) may be based on a direction of a reflected specular lobe associated with the pixel.

Abstract: There is provided an image processing apparatus that generates a virtual viewpoint image on which an image indicating a position of a virtual viewpoint, a line-of-sight direction from the virtual viewpoint, and event information indicating an occurrence position of an event occurring at a time later than a time corresponding to the virtual viewpoint image is displayed in a superimposed manner.

Abstract: A system and method enabling per-user-optimized computing, rendering, and provisioning within virtual worlds. The system comprises a server including memory and at least one processor, the memory storing a persistent virtual world system comprising a data structure in which at least one virtual replica of at least one corresponding real object is represented, and a computing optimization platform configured to store and provide rules for optimizing the computing, rendering and data provisioning to users via user devices. A plurality of connected devices connected to the server via a network provide multi-source data, user input, or combinations thereof, to the persistent virtual world system, updating the virtual replicas. The server retrieves user location, viewing position and orientation from the one or more user devices to determine a user interaction radius, thereby optimizing via the computing optimization platform the relevant computing, rendering and provisioning for the one or more user devices.

Abstract: In implementations of systems for efficiently generating blend objects, a computing device implements a blending system to assign unique shape identifiers to objects included in an input render tree. The blending system generates a shape mask based on the unique shape identifiers. A color of a pixel of a blend object is computed based on particular objects of the objects that contribute to the blend object using the shape mask. The blending system generates the blend object for display in a user interface based on the color of the pixel.

Abstract: A method for creating a 3D model of a scene from a single frame of data points representing image and/or depth data is provided, comprising the steps of: segmenting the data points into a plurality of groups of data points, each group of data points having a high probability of representing a single object; determining, for each group of data points, which method from the group of network completion methods, object assignment methods is suitable for creating a 3D half model from the group of data points; creating a 3D half model from the group of data points according to the determined method, and merging the 3D half models into an overall 3D model.

Abstract: A system includes one or more hardware processors and memory storing instructions that, when executed by the one or more hardware processors, causes the system to access image data depicting an object including generating the image data via a camera of a mobile device of the user where the object is a portion of the user, identify a set of features, assign labels to regions of the image data based on the set of features including providing the set of features identified from each respective region of the regions as input into a model and where the model outputs a label for the respective region and where the labels comprise one or more semantic labels or classifications that correspond with the set of features, and generate a three-dimensional (3D) model of the object based on the labels assigned to the regions of the image data.