5 results about "Inertial motion capture" patented technology

This invention belongs to the field of deep learning, specifically relating to a posture-induced multimodal gait recognition method aimed at improving the accuracy of gait recognition in complex environments. The method includes constructing a multimodal dataset containing gait video sequences, corresponding depth map sequences, and motion-captured posture sequences by combining a full-body inertial motion capture device, a binocular depth camera, and an RGB camera. Cross-device gait feature alignment preprocessing is performed on the multimodal dataset to obtain spatiotemporally aligned motion-captured postures and video postures. A 3D posture induction module is constructed, using the spatiotemporally aligned motion-captured postures as a reference, and trained under supervised training via graph feature alignment loss to extract induced posture information from the video postures. A multimodal recognition model fusing induced posture information and gait contour features is constructed to achieve gait recognition.

This application discloses a motion data redirection method, system, device, and storage medium. The method includes the following steps: acquiring the first skeleton coordinates of the target character, the second skeleton coordinates of the original character, the first driving quaternion of the original character in the current frame, the first root node coordinate information of the original character in the current frame, the first whole-body node coordinate information of the target character in the previous frame, and the fourth whole-body node coordinate information of the original character in the previous frame; determining the temporary second whole-body node coordinate information of the target character in the current frame; determining the third whole-body node coordinate information of the original character in the current frame; determining the node identifier with the smallest coordinate change; determining the motion change of the node corresponding to the node identifier in the target character; and adjusting the first whole-body node coordinate information of the target character in the current frame. This method can be applied to both real-time and non-real-time motion data redirection, and has strong practicality and scalability. This application can be widely used in the field of inertial motion capture technology.

This application relates to the fields of motion capture technology and computer human body model technology, and particularly to a method, device, electronic device, and storage medium for automatic adjustment of a surface model. The method includes: acquiring the posture data of a reference human body at the current moment; adjusting the current posture of the surface model of the reference human body based on the posture data to obtain the target posture of the surface model, and acquiring posture representation data of a model human body using an inertial motion capture system; determining the mapping relationship between the reference human body and the model human body, and adjusting the target posture of the surface model based on the mapping relationship and the posture representation data to obtain a dynamic surface model that changes over time. This solves the problems in related technologies where posture human body model modeling requires manual adjustment, has a low degree of automation, and where automatic adjustment of human body models based on optical motion capture is expensive, complex to operate, and has a narrow range of applications.

A robot learning dataset construction method, a humanoid robot, and a computer-readable storage medium are provided. The method includes: a robot operator uses an inertial motion capturing device to remotely operate a humanoid robot to perform various operation tasks, construct a dataset by collecting real-time working images and real-time joint motion data of the humanoid robot performing various operation tasks under manual guidance, thereby utilizing the flexible combination of the human body posture direct extraction characteristics of the inertial motion capturing device and the flexibility and reliability of executing robot remote operation tasks so as to improve the construction efficiency, dataset validity, and dataset integrity of the task learning dataset, which facilitates further improvement of the generalization and accuracy of the imitation learning of the humanoid robot.

This invention relates to a multimodal spatiotemporal alignment and interactive reconstruction method for digital twins of intangible cultural heritage skills, belonging to the interdisciplinary field of digital protection of intangible cultural heritage and computer graphics. It simultaneously acquires five modalities: optical and inertial motion capture, hyperspectral imaging, panoramic sound field, multi-view images, and physiological data of inheritors. Temporal alignment is achieved through cubic spline interpolation and Gaussian mixture model expectation-maximization algorithm, and spatial alignment is completed using point cloud registration. A dynamic graph spatiotemporal interaction network is used to generate cross-modal fusion features. Based on this, a three-level digital twin is constructed: geometric, technological, and knowledge-based. The technological twin extracts temporal evolution features based on Transformer, while the knowledge twin expresses causal transmission relationships using process hyperedges. Finally, interactive experiences are provided through dynamic geometric optimization, multimodal immersive rendering, and gesture / controller interaction, and user behavior feedback is used for incremental learning and knowledge graph updates, forming a self-evolving closed loop.