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Real-time tracking of human body motion is crucial for interactive and immersive experiences in AR/VR.
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An RNN-Ensemble Approach for Real Time Human Pose Estimation from Sparse IMUs. In Proceedings of the 3rd International Conference on Applications of Intelligent Systems (Las Palmas de Gran Canaria, Spain) (APPIS 2020) . Article 32, 6 pages
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PhysCap: Physically Plausible Monocular 3D Motion Capture in Real Time
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A scalable approach to control diverse behaviors for physically simulated characters
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Full-Body Motion From a Single Head-Mounted Device: Generating SMPL Poses From Partial Observations. In International Conference on Computer Vision 2021
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Control Strategies for Physically Simulated Characters Performing Two-Player Competitive Sports
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Transformer Inertial Poser: Real-time Human Motion Reconstruction from Sparse IMUs with Simultaneous Terrain Generation
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ASE: Large-scale Reusable Adversarial Skill Embeddings for Physically Simulated Characters
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DeepPhase: Periodic Autoencoders for Learning Motion Phase Manifolds
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Physics-Based Character Controllers Using Conditional VAEs
Jungdam Won, Deepak Gopinath, and Jessica Hodgins. 2022 · 2022
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Learning Soccer Juggling Skills with Layer-wise Mixture-of-Experts
Zhaoming Xie, Sebastian Starke, Hung Yu Ling, and Michiel van de Panne. 2022 · 2022
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Physical Inertial Poser (PIP): Physics-aware Real-time Human Motion Tracking from Sparse Inertial Sensors. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Xinyu Yi, Yuxiao Zhou, Marc Habermann, Soshi Shimada, Vladislav Golyanik, Christian Theobalt, and Feng Xu. 2022 · 2022
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