Основные понятия
The author presents SimXR, a method for controlling a simulated avatar using information from AR/VR headsets. The approach synergizes headset poses with camera images to guide body movements in real-time.
Аннотация
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.
Статистика
Due to challenging viewpoints, some work uses head tracking as an alternative [28, 30, 41, 59] for pose estimation.
Our approach achieves comparable or better pose estimation results than both prior vision and vision + physics-based methods.
Training only using synthetic data, our lightweight networks can control simulated avatars in real-world data capture with high accuracy in real-time.
Comparing R1, R2 and R5 shows the importance of each modality: vision signals provide most end-effector body movement signals while the headset guides body root motion.
Without vision signals (R1), the humanoid would achieve poor pose estimation results but can still achieve a reasonable success rate since the headset pose provides a decent amount of movement signals.
Цитаты
"Due to similar issues in the VR headset case, KinPoly-v also does not perform well."
"Our method effectively uses physics as a prior and can create plausible lower body movement based on input signals."
"SimXR achieves better performance compared to existing methods across various datasets."