125 results about "Motion capture" patented technology

Implementations disclosed herein are directed to a method for predicting motion capture data implemented by a video game development system including one or more processors. The method can include: obtaining, by one or more of the processors of the video game development system, motion capture data; the motion capture data can include one or more missing values; generating, by one or more of the processors of the video game development system, a predicted value for the one or more missing values. Generating a predicted value can include processing the motion capture data using one or more machine learning models to determine the predicted value.

A method and apparatus for supporting motion capture systems, such as markerless systems, is provided. Initial representations, such as salient points, of a physical subject are received based on camera data. Enhanced representations, such as improved salient points or vector indications, of a physical subject are then identified based on the initial representations in combination with specified physical constraints. A model can be used to specify the physical constraints, and the enhanced representations can be obtained as locations or other features of the model. The enhanced representations are thus inherently constrained by the model. The enhanced representations can be used for AI training or for other purposes.

This application discloses a method, apparatus, device, and storage medium for motion redirection, relating to the field of computer technology. The method includes: acquiring a motion capture sequence to be processed, the motion capture sequence including an upper limb motion sequence and a lower limb motion sequence; obtaining human arm span information based on a preset parametric human model and the upper limb motion sequence; converting the upper limb motion sequence based on preset arm span parameters of a target robot and the human arm span information to obtain an upper limb motion sequence of the target robot; obtaining an upper limb joint angle sequence of the target robot using an inverse kinematics algorithm based on the upper limb motion sequence of the target robot; performing information extraction processing on the upper limb joint angle sequence of the target robot to obtain three-dimensional spatial pose information corresponding to the upper limb joint angle sequence of the target robot; and determining the motion data of the target robot to be redirected based on the upper limb joint angle sequence and the three-dimensional spatial pose information of the target robot.

The application relates to the field of motion capture and virtual simulation, and particularly relates to a finger motion capture and cross identification method, which adopts a six-axis and nine-axis sensor unit, is initialized and configured through a main control board and a communication bus, is subjected to gyro zero offset calibration, completes data conversion, attitude solution and other pretreatments, transmits binary floating point data packets containing an attitude array and a time stamp to a computer through a wireless connectionless protocol, carries out physical collision simulation and IK solution based on a unified constraint solver and a three-level weight constraint mechanism, and finally generates and feeds back a display bone animation. The application adopts the six-axis and nine-axis sensor unit, is subjected to gyro zero offset calibration, data pretreatment and UDP protocol transmission, combines an XPBD framework to build a three-level weight constraint mechanism to carry out collision simulation and IK solution, realizes low-cost and accurate identification of finger crossing, realizes data non-drifting and stable transmission, and outputs a smooth bone animation which is in line with physiological limits.

This invention discloses a LiDAR human motion capture method based on Bézier curve degradation modeling, comprising: acquiring a continuous multi-frame LiDAR point cloud sequence; constructing a trajectory-aware Bézier motion degradation module to fit the original joint trajectory and gradually reduce control points through a trajectory preservation strategy to generate a multi-level motion representation from coarse to fine; designing a progressive motion reconstruction module, using a multi-timescale motion transformer (TMT) to predict Bézier motion curves at multiple time scales based on point cloud features, and using a multi-level motion aggregator (MMA) to adaptively fuse the multi-scale curves to reconstruct a detailed and temporally coherent 3D human posture sequence. This invention effectively alleviates the posture jitter or failure problem caused by occlusion, noise, and sparse point clouds, significantly improving the accuracy and temporal continuity of motion capture, and is suitable for complex open scenarios such as autonomous driving and robotics.

We provide the system. [Solution] A means of scanning a user's face in real time and acquiring feature data through a visual aid connected to a digital device, A means for analyzing selected video data and extracting facial and motion information of characters within the scene, A method for compositing the user's facial feature data onto a character's face and reflecting the user's movements in the video using motion capture technology, A means for optimizing the generated video and streaming it to a digital device, A system that includes this.

This disclosure relates to the field of image processing technology, and in particular to providing an image processing method, apparatus, device, medium, and product. In this disclosure, in response to a motion capture function, multiple frames of first images are acquired; these multiple first images are consecutive frames of original images acquired based on a first exposure time, where the first exposure time is much shorter than the first duration; image denoising processing is performed on the multiple first images to obtain a second image; stripe detection is performed on the second image to obtain an image detection result; when the image detection result indicates the presence of stroboscopic stripes, stripe removal processing is performed on the second image to obtain a target image. By denoising the consecutive frames, the image quality of the original image can be improved. Then, stripe detection is performed on the second image, and if stripes are present, they can be removed. This avoids the large amount of noise present in short-exposure images, while also satisfying the stripe phenomenon that appears under a stroboscopic light source for moving targets. Therefore, the image quality is improved.

This utility model relates to the field of mechanical fountain devices, and more particularly to a human-computer interactive sensory fountain. Addressing the problem that existing fountains typically spray water in a cylindrical shape, but the water jet has a large impact force when it first exits the nozzle, easily hitting nearby children and causing injury to their face or eyes, this utility model proposes the following solution: a protective ring, a fountain nozzle, an atomizing nozzle, a closed water storage pipe, a ring plate, a drive structure, and a motion capture camera. In this utility model, an infrared ranging sensor monitors the distance between the child and the protective ring. Within the monitoring range of the infrared ranging sensor, when the child approaches the protective ring, the infrared ranging sensor sends an electrical signal to the first motor. At this time, the rotation shaft of the first motor rotates clockwise, causing the fountain nozzle to stop spraying water, while the atomizing nozzle begins to spray water mist, preventing the water jet from the fountain nozzle from causing injury to the child.

The application discloses a pose measurement and motion control device for a spacecraft simulator, comprising a three-degree-of-freedom air floating table, the three-degree-of-freedom air floating table comprising an air floating table base and an air floating table mounting seat; a momentum wheel, a first inertia measurement unit and a momentum wheel unloading fan are arranged on the air floating table mounting seat; a pose adjustment fan and a second inertia measurement unit are arranged on the air floating table base; the pose measurement and motion control device further comprises an optical motion capture system for measuring the pose and linear velocity of the air floating table base and an industrial computer for controlling the running of a program algorithm and the issuing of component control instructions. The application can realize high-efficiency and high-precision control of the attitude angle, position of the three-degree-of-freedom air floating table and the rotation angular velocity of the spacecraft simulator, and can simultaneously measure and control multiple targets.

The present specification provides a video production method and device, wherein the video production method comprises: acquiring motion capture data and action video, and identifying the action video to obtain skeleton point data; generating a virtual image video according to the motion capture data, and adding the skeleton point data to the action video to obtain a skeleton point video; importing the virtual image video and the skeleton point video into an editor; fusing the virtual image video and the skeleton point video through the editor to obtain a target video containing determination information; wherein the determination information corresponds to the skeleton point data.

The present application relates to a kind of 3D twin intelligence examination device and system to stab training, belong to intelligent training examination technical field, including inductive protective clothing, no wearing motion capture terminal, smart bracelet, multi-source data synchronization module, intelligent simulator, triplex screen host, the inductive protective clothing includes multiple combined armors and abdominal armor, multiple the combined armors are equipped with protective bending part and fixing belt part, several the fixing belt part is formed by the fastening structure of elastic band and velcro combination, multiple protective bending part includes fixed bending plate, elastic protective pad and conversion convex shell, the present application solves the problem of single data dimension in traditional assassination training examination, big artificial error in judgment, no tactical analysis, training injury prevention and control loss, AI guidance blank and poor review effect, realize the digitization, standardization, intelligentization of assassination training examination, while guaranteeing data local processing, satisfy information security requirement.

The application provides a kind of theft behavior identification method and system in shelf scene, belong to action recognition technical field, this method includes: obtaining the monitoring video stream in shelf scene;The human key point information of each image frame in monitoring video stream is obtained by key point detection algorithm, and at least one image frame subsequence is obtained by non-overlapping segmentation monitoring video stream;Based on the human key point information of the image frame contained in each image frame subsequence, the corresponding action feature representation is obtained;Determine the semantic similarity between each standard feature representation in the standard feature representation-word mapping relationship of each action feature representation, and determine the corresponding word of each action feature representation based on semantic similarity, to obtain the word sequence corresponding to the monitoring video stream of each action feature representation;Word sequence is input into semantic classification model, and the theft behavior identification result of monitoring video stream is output to obtain.The application can realize high reliability normal action recognition and theft action capture in shelf scene.

This invention relates to the field of medical device technology, and more particularly to a personalized trajectory planning system for stroke rehabilitation that integrates electromyography and motion capture. The system includes a power supply module, and a multimodal perception enhancement module, an intelligent fusion decision-making module, a dynamic trajectory generation module, a clinical adaptation enhancement module, and an effect evaluation feedback module electrically connected to the power supply module. The multimodal perception enhancement module, intelligent fusion decision-making module, dynamic trajectory generation module, and clinical adaptation enhancement module are sequentially connected via data. The effect evaluation feedback module is data-connected to the clinical adaptation enhancement module and establishes a closed-loop data interaction link with the dynamic trajectory generation module. This invention achieves accurate acquisition, information complementarity, and time synchronization of multi-source perception data, providing high-quality and consistent input data for subsequent decision-making. It solves the problems of single perception data, poor path adaptation to the patient's actual situation, and increased surgical risks in existing technologies.

A system is disclosed that is able to combine motion capture data with volumetric capture data to capture player style information for a player. This player style information or player style data may be used to modify animation models used by a video game to create a more realistic look and feel for a player being emulated by the video game. This more realistic look and feel can enable the game to replicate play style of a player. For example, one soccer player may run with his elbows closer to his body and his forearm may swing across his torso. While another soccer player who is perhaps more muscular may run with his elbows and arms further from his body and his forearms may not cross in front of his torso when running.

The application discloses a gait real-time analysis method and system based on visual skeleton feature extraction, which comprises the following steps: collecting a user walking video through a single camera; extracting human key skeleton points in real time by using a lightweight AI model; calculating anatomical coronal plane, sagittal plane parameters and gait space-time parameters based on the skeleton point coordinates; superimposing the skeleton and parameters on the video in real time and providing voice feedback; starting and stopping through a gesture recognition non-contact control system; automatically storing data and generating a rehabilitation report; the system comprises a USB camera, an embedded processing end and a feedback end with display and audio; the application replaces expensive motion capture equipment with a low-cost single camera, realizes objective quantitative evaluation of gait training, real-time multi-modal feedback and automatic management, significantly reduces medical costs, improves the efficiency and effect of rehabilitation training, and is especially suitable for primary medical institutions.

Some examples of the disclosure are directed to a virtual motion capture application configured to operate on a first electronic device. In some examples, the virtual motion capture application is configured to present, via the first electronic device, a user interface element containing an animation timeline and a virtual platter including a virtual object in a computer-generated environment. In some examples, the user interface element includes a plurality of options and tools for generating pose-centered motion for the virtual object in the computer-generated environment. In some examples, the virtual motion capture application is configured to exchange data corresponding to the virtual object with a content creation application running on a second electronic device. In some examples, the second electronic device may display a motion creation user interface associated with the content creation application while the first electronic device presents the computer-generated environment.

This application discloses a method for identifying reflective marker clusters in robot swarms. The method includes: acquiring the coordinate information of each binary marker captured by an optical motion capture system; performing Euclidean clustering on each binary marker based on its coordinate information to obtain multiple clusters, each cluster corresponding to a reflective marker cluster, and each cluster containing multiple binary markers; acquiring the marker cluster type and code length information corresponding to each reflective marker cluster based on its radius information; and identifying each reflective marker cluster based on its cluster type and code position information to obtain the corresponding code position information. Self-labeling is achieved based on the inherent characteristics of the marker clusters: each cluster contains unique encoded information, and the motion capture system can directly decode the cluster ID by detecting the relative layout of points within the cluster, achieving automatic labeling without manual intervention or additional matching steps.

In various examples, systems and methods of the present disclosure may independently control different components of a robotic system—such as a humanoid robot—by using sensors to track the movements of a teleoperator. For instance, a motion capture glove(s) worn by a teleoperator may be used to track the motion of the teleoperator's hand complex(es), while a spatial computing device (e.g., an Augmented Reality (AR), Virtual Reality (VR), and / or Mixed Reality (MR) headset) may be used to track the motion of the teleoperator's non-manual body parts. The sensor data from these different modalities may be used to map the motions of the teleoperator to corresponding components (e.g., body parts) of the humanoid robot. In some instances, a video depicting a first-person perspective view associated with the humanoid robot may be live streamed to the teleoperator for feedback while the teleoperator is controlling the humanoid robot.

The application discloses a humanoid robot intelligent grabbing control system and method, and relates to the technical field of humanoid robots.The intelligent grabbing control system comprises an electromyographic sensing control device and a humanoid robot device, wherein the electromyographic sensing control device communicates with the humanoid robot device; the electromyographic sensing control device comprises an electromyographic signal sensing module, a gesture posture acquisition module, a motion capture module, a grabbing control module and an AR module; and the humanoid robot device comprises a mechanical hand, a camera mechanism assembly, a mechanical arm, a camera mechanism movement driving module, a target object three-dimensional coordinate and posture calculation module, a target object posture recognition module, a first position calculation module, a second position calculation module, a mechanical hand position verification module and a mechanical hand posture acquisition module.The humanoid robot intelligent grabbing control system and method, electronic equipment and storage medium provided by the application can remotely control the action of a humanoid robot, and improve the accuracy and work efficiency of control.

We provide amusement facilities that offer services combining digital point management and motion capture technology. [Solution] In the CPU 11 of the management server 1, the information management unit 32 manages identification information associated with each predetermined medium, the generation unit 33 generates skeletal information about the user's skeleton from the detection results of the user's body movements, the point management unit 35 assigns predetermined points according to the content of the skeletal information, and the information management unit 32 manages the generated skeletal information and the assigned point values ​​in association with the identification information of the medium presented by the user. This solves the above problem.

The application relates to the technical field of motion capture and motion recognition, and discloses a real-time athlete posture analysis method based on computer vision, which comprises the following steps: extracting an angular acceleration feature of a posture sequence of a motion source subject, recognizing a posture motion phase, mapping a load point, and generating a dynamic load characteristic curve; monitoring a deformation driving unit current signal, determining an absolute zero position of a controlled deformation part by using a current surge, and establishing a physical alignment relationship; determining a compensation stroke value according to a change rate of the angular acceleration feature and a delay time of an execution module, correcting the alignment relationship, and generating a target deformation procedure, so that the execution module can implement a pressing action at a load peak point. Through the establishment of dynamic coupling between the motion phase and the deformation position, the application solves the problem of space-time deviation between a static model and a dynamic stress point; and by using an electric signal alignment mechanism, displacement drift can be offset, and the alignment accuracy of a feature point can be ensured.

The present application relates to the technical field of computer vision and sports data analysis, and provides a sports action evaluation method based on a space-time graph attention network, comprising: acquiring continuous action capture data of a target sports action performed by a test object; constructing a human body power chain graph according to the kinematic connection relationship between a plurality of joints and the joint synergistic force relationship corresponding to the target sports action, and processing the continuous action capture data to obtain a to-be-tested action space-time representation sequence; dividing the action stages based on the to-be-tested action space-time representation sequence to obtain at least two action stages corresponding to the target sports action; determining the action deviation source in each action stage in combination with the human body power chain graph, and determining the evaluation result of the target sports action performed by the test object according to the associated path of the action deviation source in the human body power chain graph.

This invention discloses a wargaming touch control method and system based on motion capture and projection fusion, belonging to the field of wargaming technology. Through projection fusion technology, the wargaming materials are projected onto the target wargaming area, and the various wargaming distribution maps are fused and projected, greatly expanding the projection interface of the wargaming projection and supporting the needs of large-scale wargaming. Furthermore, the system captures the target operator's action video stream in real time, establishing an operator action model, achieving real-time and accurate motion capture, and obtaining complete and accurate operator action information. Moreover, a pre-trained operator action recognition model analyzes the target operator's action information in real time, identifying the corresponding action recognition results. Based on the action recognition results, the target wargaming object is selected, and the corresponding wargaming update parameters are calculated, completing precise wargaming touch control execution.

This invention discloses an intelligent scoring system and method for self-rescue and mutual-rescue assessments. The scoring system includes a wearable motion capture module for real-time acquisition of acceleration, angular velocity, and attitude angle data of various parts of the human body; a 3D somatosensory depth camera and infrared reflective markers for capturing three-dimensional spatial coordinate information; a data fusion and attitude estimation module for inertial data attitude estimation and calculation of joint angles of various joints of the human body; a feature extraction and action recognition module for identifying the current action type and the quality of action completion; and an intelligent scoring and feedback module for automatically scoring the identified action sequence based on preset scoring items and generating scoring results, while outputting scoring details and action optimization feedback. The scoring system and method described in this invention can achieve unified scoring standards, accurate feedback, adaptability to large-scale training and assessments, and adaptability to complex emergency rescue scenarios.

This invention relates to the fields of computer and virtual reality technology, and discloses a method for virtual reality reproduction of opera performances based on motion capture technology. This method uses inertial and optical motion capture, eye tracking, and facial expression acquisition to fuse multi-source heterogeneous data into a 4D temporal motion dataset; it combines a stylized opera motion library for semantic annotation, and uses a graph convolutional neural network to construct a mapping model from motion to digital human driving parameters; it drives a high-fidelity opera digital human in a virtual reality environment, synchronously rendering costumes, makeup, dynamic fabrics, and stage lighting and sound effects that conform to the characteristics of different opera roles, achieving high-precision reproduction of hand, eye, body, and footwork.

The application belongs to the field of motion capture data recovery, and provides a human motion capture data recovery method and system, which comprises the following steps: acquiring a motion capture data matrix, wherein the motion capture data matrix is composed of sequentially arranged frames; mapping each frame of data in the motion capture data matrix to a high-dimensional feature space based on linear projection, embedding spatial position information into the frame of data, and obtaining high-dimensional single-frame data; extracting spatial features of the correlation between all marker points in the high-dimensional single-frame data by using a self-attention mechanism, and obtaining high-dimensional single-frame spatial features; embedding time information into all high-dimensional single-frame spatial features of the motion capture data matrix, obtaining time features of the correlation between the high-dimensional single-frame spatial features by using the self-attention mechanism, and obtaining a time-space motion capture data matrix; and restoring complete motion capture data by using a multilayer perception based on the time-space motion capture data matrix.

The application provides a motion capture actor fitness evaluation method and system based on a neural network, which comprises the following steps: based on an input role description text, extracting a semantic representation of the text, and mapping the semantic representation to obtain a role style vector; based on the role style vector, converting the role style vector into target motion structure features through a motion structure generation network; based on motion capture data of an actor, simultaneously extracting an actor style vector and an actor structure feature from the motion capture data through a double-branch neural network; and based on the role style vector, the target motion structure features, the actor style vector and the actor structure feature, calculating a motion fitness score between the actor and the role through a unified scoring model for fusing style and structure.