5943results about "Image generation" patented technology

An example operation may include at least one of receiving, via a user interface of a device, an activation input from a user to initiate a session, capturing, by a camera of the device, a scan of a body of the user, wherein the capturing comprises recording at least one image and / or at least one video of the user, processing the at least one image and / or video to generate a three- dimensional model of the user comprising measurements and contours of the body, retrieving, from a database, at least one clothing item associated with the user, the at least one clothing item comprising dimensional attributes and texture attributes, rendering, by a graphics processing unit, the at least one clothing item onto the three-dimensional model to generate a visual representation, wherein the rendering simulates draping behavior, movement, and light interaction of the at least one clothing item relative to the three-dimensional model, and displaying, on the user interface, an interactive visualization comprising the visual representation of the three-dimensional model with the at least one clothing item from multiple viewing angles.

The invention relates to the technical field of medical rehabilitation, and discloses a lower limb weight-bearing gait rehabilitation training system which comprises a data acquisition module, a data processing and analysis module, a patient individualized modeling module, an intelligent decision and control module, a rehabilitation execution module and a man-machine interaction and medical information interface module which are in communication connection through a network. The data acquisition module is used for acquiring multi-modal data of a patient in real time, and the multi-modal data comprises static sign data, dynamic physiological parameters, kinematics and dynamics parameters and non-motion physiological and psychological state data; and the data processing and analysis module is used for carrying out preprocessing, feature extraction and deep analysis on the original data, and outputting a structured patient individualized feature vector and an evaluation result. According to the invention, a patient three-dimensional skeletal muscle digital twinborn model is constructed through the patient individualized modeling module, and in combination with a continuous learning intelligent model library, body sign differences of different patients can be accurately adapted.

Computer-implemented method of training a machine learning system for generating images from a predefined layout. The machine learning system is a diffusion probabilistic model with an encoder part and a decoder part, wherein a normalization layer in a residual block of the decoder part comprises weighted layout-aware affine transformation parameters γ and β, determined from layout-aware affine transformation parameters γ' and β' by multiplication with a weighted semantic map, wherein the weighted semantic map comprises a sum of two contributions, wherein the first contribution comprises a non-overlapping semantic map computed from determined and size-ranked object probabilistic masks and wherein the second contribution comprises an edge-aware semantic map, determined from extended object probabilistic masks, wherein an extended object probabilistic mask is extended with respect to the corresponding object probabilistic mask by one pixel along the borders.

A system for computer assisted navigation during surgery includes a computer platform that operates to register a target surgical area of a patient. In certain cases, a process includes: obtaining a pre-op CT image of a pelvic region of a patient and intra-operatively obtaining a point cloud data about the pelvic region with a navigated instrument, generating a 3D bone model which excludes non-targeted area such as a femur, and then merging the 3D bone model to the point cloud to register the target surgical area.

A programmatically generated AI avatar includes a customizable personality module, acting as the embodied interface for a powerful AI “mind” that delivers personalized coaching to improve user health, well-being, and longevity. The system uses machine learning, large language models, and biometric modeling to synthesize real-time, multi-modal health data—including sleep, nutrition, glucose, mood, and activity—and generate forward-prescribed KHAs. Unlike human coaches, it continuously adapts based on context and behavior, targeting the root cause: metabolic dysfunction—namely by restoring healthy, sustainable body composition through the preservation or building of lean muscle mass and reduction of excess fat. KHAs can also be shared with friends or programmatically generated AI avatars, allowing for coordinated action, emotional support, and accountability through social connection—further reinforcing positive behavior and adherence. The system's reinforcement learning engine incorporates both individual response data and anonymized population-level insights to optimize recommendations over time, learning which interventions are most effective for users with similar physiological and behavioral profiles. First validated with Olympic athletes—resulting in measurable improvements and medal-winning outcomes—this system offers a scalable, emotionally intelligent coaching engine that exceeds human capability, designed for the ultimate purpose of supporting sustainable health, resilience, and human thriving.

A perception system may be used to generate bounding boxes for objects in a vehicle scene. The perception system may receive images and feature maps corresponding to the received images. The perception system may correlate object queries from previous time steps with object queries from the current time step.

A system for computer assisted navigation during surgery includes a computer platform that operates to register a target surgical area of a patient. In certain cases, a process includes: obtaining a pre-op CT image of a pelvic region of a patient and intra-operatively obtaining a point cloud data about the pelvic region with a navigated instrument, generating a 3D bone model which excludes non-targeted area such as a femur, and then merging the 3D bone model to the point cloud to register the target surgical area.

In various examples, a framework is or provides an end-to-end solution that includes multi-sensor capture, data processing, inferencing, synchronization, alignment, and 3D rendering for multi-modal perception fusion pipelines. A multi-modal perception fusion pipeline may include a mixer, an aligner, an inference environment, and a multi-view renderer. The mixer may merge sensor data from different data sources into a single HashMap frame. The aligner may use calibration data for sensor-to-sensor coordinate transformations. The inference environment may receive multi-modality data and use custom preprocessing and custom postprocessing to generate inference results. The renderer may generate different sensor data renderings. The framework may include an application that uses configuration data to generate or configure a custom multi-modal perception fusion pipeline. The inference environment may access inference models using a uniform inference interface and support remote inference, allowing the pipeline to become an API client of the inference models.

The invention belongs to the technical field of three-dimensional scene reconstruction, and particularly discloses a multi-view three-dimensional Gaussian densification method and system for adaptive density control, and the method comprises the following steps: collecting a multi-view original image, and carrying out the preprocessing of the multi-view original image; complexity features are extracted, a pixel-level complexity heat map is generated, and a globally unified three-dimensional complexity field is constructed; performing back projection on the reconstruction residual error, high-frequency inconsistency and depth / geometric consistency cost of each view angle, generating three-dimensional error popularity, determining a candidate newly-added set and a candidate pruned set, generating a weak label to train a lightweight multilayer perceptron classifier, outputting a ternary probability corresponding to newly-added / pruned / maintained, and obtaining a new / pruned / maintained three-dimensional perceptron classifier; and performing Gaussian densification operation on the newly added region. By adopting the technical scheme, fine point adding is carried out on the complex area, effective pruning is carried out on the simple area, and meanwhile, the synthesis quality, the global consistency and the calculation efficiency of the new view angle are improved.

The invention discloses a space identification method and system based on a YOLO model, a terminal and a medium, and the method comprises the steps: obtaining a CAD drawing, carrying out the space segmentation and coordinate conversion of the CAD drawing, obtaining a PNG image corresponding to a specified space region in the CAD drawing and a label file corresponding to the PNG image, and determining a training data set based on the PNG image and the label file; building a Python operation environment, installing a YOLOv8-seg model and a related dependency library thereof, and carrying out end-to-end training on the training data set based on a training script of the YOLOv8-seg model to obtain a YOLO space segmentation model; and based on the YOLO space segmentation model, carrying out space segmentation on the building plane drawing, outputting a segmentation result, and carrying out automatic labeling on a space region. According to the method, the YOLO space segmentation model is adopted, high-precision space recognition and classification are achieved, and the generalization ability is high.

A system for computer assisted navigation during surgery includes a computer platform that operates to register a target surgical area of a patient. In certain cases, a process includes: obtaining a pre-op CT image of a pelvic region of a patient and intra-operatively obtaining a point cloud data about the pelvic region with a navigated instrument, generating a 3D bone model which excludes non-targeted area such as a femur, and then merging the 3D bone model to the point cloud to register the target surgical area.

A system for computer assisted navigation during surgery includes a computer platform that operates to register a target surgical area of a patient. In certain cases, a process includes: obtaining a pre-op CT image of a pelvic region of a patient and intra-operatively obtaining a point cloud data about the pelvic region with a navigated instrument, generating a 3D bone model which excludes non-targeted area such as a femur, and then merging the 3D bone model to the point cloud to register the target surgical area.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as automatic driving and intelligent transportation, financial science and technology, medical health and the like, and discloses a three-dimensional scene event analysis method, device, equipment and medium. Generating a three-dimensional bounding box for a dynamic target in each frame of multi-view image data, fusing the information of the three-dimensional bounding box with the features of the semantic graph, generating a refined three-dimensional voxel semantic graph, aggregating multiple frames of refined three-dimensional voxel semantic graphs, constructing a motion track of the dynamic target, and detecting a space overlapping event based on the motion track. And generating a collision event description based on the relative motion state, and generating an attribution analysis result of the event. According to the method, the accuracy and automation level of collision detection and responsibility analysis in a dynamic scene are improved by fusing the multi-frame image information and the three-dimensional voxel data and combining the target position and the motion state.

The present invention provides a system and method for generating proposals for infrastructure modalities, such as electrical substations, using advanced artificial intelligence. It includes an input interface for data collection, a lightweight generative or rendering pipeline for creating preliminary 2D designs, and a generative model selected from diffusion, transformer-based, GAN or other architectures for refining these into detailed 3D models and generating preliminary designs. The system evaluates designs against predefined criteria to ensure compliance and feasibility. Supported by a cloud-based infrastructure for robust data processing and integration with third-party services, this system enhances the efficiency, accuracy, and compliance of modality planning and proposal generation.

Disclosed is a image processing apparatus and a method for controlling the image processing apparatus. The image processing apparatus according to an embodiment of the present disclosure may identify an object from an acquired image, determine whether the object is hidden by another object by using an aspect ratio of a bounding box of the detected object, and based on the object being hidden, estimate an entire length of the object based on coordinate information of the bounding box. Accordingly, the size information of the hidden object may be efficiently identified while a large amount of database is applied or resources of the apparatus is minimized. The present disclosure may be in connection with a surveillance camera, an automotive driving vehicle, an artificial intelligence module of at least one of a user terminal or a server, a robot, an augmented reality (AR) device, a virtual reality (VR) device, a device related to a 5G service, and the like.

The invention provides an unmanned aerial vehicle pose visual angle optimization method and system for fracture refined shooting. The method comprises the steps of performing fracture detection and boundary extraction on a coarse inspection image; recovering a camera track and sparse point cloud based on multi-view three-dimensional reconstruction, carrying out back projection and estimating a normal vector of a crack surface; constructing a shooting spherical shell with limited inner and outer radiuses by taking the crack point as a center, and generating a view cone and spherical shell intersection region allowed to be shot as a candidate set in combination with a normal vector; sampling in the candidate area to generate a plurality of candidate shooting points, and synchronously resolving the flight and holder integrated pose of the corresponding unmanned aerial vehicle; constructing a multi-target cost function including path length, attitude, pan-tilt angle and shooting error, and generating an optimal shooting point sequence and an inspection path through an optimization algorithm; the system realizes fine, efficient and automatic shooting of cracks in a complex structure environment through cooperation of multiple modules, and effectively improves the imaging quality and the detection precision.

Building on U.S. Provisional Patent Application No. 63 / 693,803, paragraphs for 2D-to-3D conversion and [0023]-[0024] for secure content verification, the Matrix 3D AI Polygon Mesh Hash Key System revolutionizes transforming 2D content into secure, interactive 3D AR / VR assets. Integrating AI and LiDAR, it enables real-time 3D mesh generation with precise geospatial anchoring. Users can reshape 3D content instantly via natural language commands, enhancing interactivity. Quantum-resistant encryption, using AES-256 and CRYSTALS-Kyber, ensures robust asset security. The system excels in gaming, surveillance, content verification, military operations, space exploration, deepfake detection, and pharmaceutical research, offering unmatched precision and scalability. Its multi-stage rendering pipeline, powered by distributed AI-driven bots, supports efficient real-time 3D mesh generation, achieving 95% accuracy and 1 cm resolution. This AR / VR technology advancement transforms industries with scalable, secure solutions for interactive 3D content creation and management, setting a new standard for precision and versatility.

A digital twin-based intelligent slope monitoring method, system, and storage medium are provided. The method includes obtaining point cloud data of a surface of a slope, creating a digital twin model; dividing the digital twin model to generate a plurality of first grids, calculating a displacement change value of each first grid during a target duration; defining the first grid with a displacement change value greater than a first threshold as a first region, dividing the target duration into a plurality of first durations with different lengths; setting a plurality of rainfall patterns, determining an optimal pattern for each first duration, calculating representative rainfall, constructing a prediction model, predicting representative rainfall for a future duration based on the optimal pattern and the representative rainfall of the first duration; generating a safety factor of the first region during the future duration based on the representative rainfall and a pore water pressure.

Systems, methods, and other embodiments described herein relate to altering an image and propagating changes to other images of the same object using a diffusion model. In one embodiment, a method includes acquiring object images depicting an object. The method includes, responsive to altering one of the object images into an edited image, adapting the object images to reflect changes in the edited image by iteratively applying a diffusion model to the object images until satisfying a consistency threshold. The method includes providing the object images to represent an edited version of the object.

The invention relates to the technical field of building templates, and particularly provides a building template flatness threshold partition detection method based on normal vector constraint RANSAC, which comprises the following steps: performing three-dimensional laser scanning on a building template to obtain high-density point cloud data; preprocessing operations such as denoising and downsampling are carried out on the collected point cloud data, so that the data quality is improved; fitting a reference surface of the template based on the preprocessed point cloud data by using an improved RANSAC algorithm, and introducing a unit normal vector to ensure the correct direction of the reference surface; calculating the vertical distance from each point cloud point to the reference surface to obtain a deviation value; independently setting a deviation threshold value according to a building specification, dividing the deviation value into different grades, and distinguishing and displaying the deviation value on the three-dimensional point cloud model through colors; and a visual result containing the three-dimensional point cloud model and the color identification is output, the flatness condition of the template is visually displayed, and the method has the effects of automatic measurement, high efficiency and high precision.

Certain aspects of the present disclosure provide techniques and apparatus for generating responses to large input prompts using a generative artificial intelligence model. An example method generally includes receiving an input prompt for processing using a generative artificial intelligence model. The input prompt is partitioned into a plurality of sub-prompts based on contextual information associated with tokens in the input prompt. A response to the input prompt is generated using the generative artificial intelligence model based on the plurality of sub-prompts and the contextual information associated with the tokens in the input prompt. The generated response is output.

The invention relates to the technical field of optical localization, in particular to an optical localization feedback control system in a minimally invasive surgery of bile adipoma. The system comprises the following steps: an image optimization module is used for scanning a full-band optical image in an operation cavity, performing adaptive contrast optimization and constructing a spectrum optimization image; the focus positioning module is used for performing accurate focus positioning on the spectrum optimization image and extracting a focus optical contour; the three-dimensional reconstruction module is used for performing structural feature point identification and three-dimensional reconstruction on the spectrum optimization image to construct a three-dimensional structure model; and the visual error compensation module is used for performing multi-angle optical ranging and stereoscopic visual error compensation on the optical contour of the focus to obtain a three-dimensional compensation positioning coordinate. According to the invention, through real-time accurate position positioning, the instrument response speed and the tissue identification precision are improved.

The invention discloses a WebGL-based mass data particle system rendering method and device, and belongs to the technical field of computer graphic processing. The method comprises the following steps: firstly, selecting the type of a particle emitter according to scene requirements, then constructing a primitive object based on points or quadrangles, and configuring three-dimensional coordinates and texture parameters; aggregation rendering is carried out on a single-frame primitive array through a WebGL vertex shader and a fragment shader, and the drawing calling frequency is reduced; the method comprises the steps of dynamically adjusting particle attributes, including position compensation, color gradient and explicit-implicit control, determining a rendering form and playing time of an animation according to requirements of an actual application scene, and supporting fine configuration of a single particle, including texture animation, dynamic scaling and rotation parameters. When a large amount of data is processed, on the premise of ensuring the rendering quality, the rendering efficiency can be remarkably improved, the consumption of a memory and GPU resources can be reduced, the response time of the system can be optimized, and meanwhile, the interaction experience of a user can be improved.

In some embodiments, an electronic device presents an M R environment including real content and / or virtual content. In some embodiments, a client application provides an API with a target region of the MR environment, one or more criteria, and a presentation effect. In response to the one or more criteria being satisfied, the electronic device presents the target region of the MR environment with the presentation effect.

A method and system provide the ability to segment a first point cloud. The first point cloud is rendered into multiple two-dimensional (2D) images. The images are segmented to generate a semantic segmentation mask. The images are then backprojected into a 3D classified point cloud. The classified point cloud is segmented into geometric segments and voting is performed for each segment to determine the majority classification and reassign minority classifications. A final point cloud is then exported as a segmented classified point cloud.

The invention provides an airspace simulation deduction method and system based on digital twinning, and belongs to the technical field of airspace simulation, and the method comprises the steps: integrating geographic bottom plate data and airspace element data, carrying out the meshing based on an airspace grid engine, and constructing an airspace digital twinning model; receiving at least one flight plan, and mapping the flight plan to the airspace digital twin model to generate a corresponding simulation aircraft entity; based on a preset simulation engine, driving the simulation aircraft entity to perform flight simulation in the airspace digital twin model, and calculating a simulation flight situation; in the simulation process, airspace conflict detection and early warning are carried out based on the simulation flight situation and the airspace digital twin model; and outputting a simulation deduction result, performing three-dimensional visual output on the simulation flight situation data and the early warning information, and generating an analysis report including conflict statistics and airspace evaluation. The airspace resource allocation is optimized, the flight safety is improved, and intelligent and efficient management is realized.

A method and system for constructing a three-dimensional (3D) aerial survey of a city street scene include obtaining a plurality of video frames from a calibrated multi-camera setup covering a 360-degree view mounted on a moving vehicle. The plurality of video frames is split into a plurality of 3D parts containing a subset of the plurality of video frames and preprocessing the subset of the plurality of video frames of each part of the plurality of parts to obtain a calculated information. Further, constructing, by the processing circuitry, a 3D representation of each part of the plurality of parts based on the calculated information to obtain a plurality of local 3D reconstructed scene intervals. The method includes stitching and filtering, by the processing circuitry, the plurality of local 3D reconstructed scene intervals to construct the 3D city street scene.

A video generation framework that is controllable, unsupervised and based on events (CUBE) includes an event camera, which captures changes in light intensity at each pixel of a scene asynchronously and generates event camera data. A text-to-image diffusion model that is conditioned on textual descriptions integrates the event camera data to control video synthesis. Further, an edge extraction module translates event data into a format usable by the text-to-image diffusion model, whereby the diffusion model synthesizes detailed and contextually accurate videos based on textual prompts. Further, an improved system (CUBE Plus) includes a content frame identification module which selectively identifies and uses only the most information-rich event segments of the event camera data to drive cross-frame attention, and an event driven attention mechanism that allows the framework to focus on event-dense moments.

Techniques are described for automatically generating visual model representations of buildings based at least in part on captured external imagery of the buildings, and using the generated building visual models to generate and present new images and optionally in additional manners, such as to improve navigation of a building and / or its surroundings. The described techniques may include acquiring building data from a plurality of exterior acquisition locations at multiple heights and view angles of an exterior of a building (e.g., using a flying drone and / or other flying device that captures the data), generating visual model representation(s) of the building (e.g., a 3D Gaussian Splat model), and using the generated visual model representation(s) to generate and present a new image with a view of the building exterior from a particular pose along with associated user-manipulatable controls.

The invention relates to the field of three-dimensional scene rendering, in particular to a three-dimensional scene data generation method, an analysis method, rendering equipment and a computer storage medium. According to the method, LOD (Level of Detail) grading processing is performed on three-dimensional scene data, foreground data in the three-dimensional scene data is divided into a plurality of LOD layers, each LOD layer is divided into a plurality of grid nodes, and each grid node is independently stored, so that the data can be loaded as required. Meanwhile, different compression modes are adopted for different data, the GPU can directly analyze and read the compressed data, decompression in a memory is not needed, occupation of the memory and video memory bandwidth is greatly reduced, and rendering efficiency is improved.