SimXR is introduced as an end-to-end method that controls a humanoid based on headset pose and camera input. The framework aims to address challenges in full-body pose estimation from head-mounted devices, offering promising results on both synthetic and real-world datasets.
The content discusses the importance of vision signals and headset poses in controlling avatars, highlighting the effectiveness of SimXR in achieving accurate pose estimations. The method leverages physics simulation and distillation to train the controller efficiently.
Key points include the use of synthetic data for training, the comparison with existing methods like UnrealEgo and KinPoly-v, ablations to analyze components' impact, and failure cases illustrating limitations in hand or feet positioning.
Overall, SimXR demonstrates potential for real-time avatar control from XR sensors, showcasing advancements in virtual reality technology.
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by Zhengyi Luo,... alle arxiv.org 03-12-2024
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