![]() Andrew Gilbert, Matthew Trumble, Charles Malleson, Adrian Hilton, and John Collomosse.In Proceedings of the IEEE 1996 Virtual Reality Annual International Symposium. Inertial head-tracker sensor fusion by a complementary separate-bias Kalman filter. ![]() Simultaneous localization and mapping: part I. In International Conference on Computer Vision 2021. Full-Body Motion From a Single Head-Mounted Device: Generating SMPL Poses From Partial Observations. Andrea Dittadi, Sebastian Dziadzio, Darren Cosker, Ben Lundell, Tom Cashman, and Jamie Shotton.Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, and Ilya Sutskever.Computationally Efficient Adaptive Error-State Kalman Filter for Attitude Estimation. Del Rosario, Heba Khamis, Phillip Ngo, Nigel H. Vasileios Choutas, Federica Bogo, Jingjing Shen, and Julien Valentin.International Journal of Computer Vision 128 (06 2020). Real-Time Multi-person Motion Capture from Multi-view Video and IMUs. Adrian Hilton Charles Malleson, John Collomosse.In 2021 IEEE Virtual Reality and 3D User Interfaces (VR). Egocentric Human Body Motion Reconstruction Using Only Eyeglasses-mounted Cameras and a Few Body-worn Inertial Sensors. Young-Woon Cha, Husam Shaik, Qian Zhang, Fan Feng, Andrei State, Adrian Ilie, and Henry Fuchs.IEEE Transactions on Pattern Analysis and Machine Intelligence (2019). OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. In Proceedings of the ACM Symposium on Virtual Reality Software and Technology(VRST ’01). Inertial and Magnetic Posture Tracking for Inserting Humans into Networked Virtual Environments. In Proceedings of the 13th European Conference on Visual Media Production (CVMP 2016)(CVMP 2016). Real-Time Physics-Based Motion Capture with Sparse Sensors. Sheldon Andrews, Ivan Huerta, Taku Komura, Leonid Sigal, and Kenny Mitchell.International Conference on 3D Vision (3DV)(2021). A Spatio-temporal Transformer for 3D Human Motion Prediction. Emre Aksan, Manuel Kaufmann, Peng Cao, and Otmar Hilliges.We evaluate our framework extensively on synthesized and real IMU data, and with real-time live demos, and show superior performance over strong baseline methods. an algorithm to generate regularized terrain height maps from noisy SBP predictions which can in turn correct noisy global motion estimation. a simple yet general learning target named "stationary body points” (SBPs) which can be stably predicted by the Transformer model and utilized by analytical routines to correct joint and global drifting, and 3. a conditional Transformer decoder model giving consistent predictions by explicitly reasoning prediction history, 2. We propose a novel method to simultaneously estimate full-body motion and generate plausible visited terrain from only six IMU sensors in real-time. Still, challenges remain such as temporal consistency, drifting of global and joint motions, and diverse coverage of motion types on various terrains. Without the ability to acquire position information directly from IMUs, recent works took data-driven approaches that utilize large human motion datasets to tackle this under-determined problem. six) wearable IMUs provides a non-intrusive and economic approach to motion capture. Real-time human motion reconstruction from a sparse set of (e.g.
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