论文标题
Questsim：来自模拟化身的稀疏传感器的人体运动跟踪
QuestSim: Human Motion Tracking from Sparse Sensors with Simulated Avatars
论文作者
论文摘要
人体运动的实时跟踪对于AR/VR中的互动和沉浸式体验至关重要。但是，有关人体的传感器数据非常有限，可以从独立的可穿戴设备（例如HMD（头部安装设备）或AR眼镜）获得。在这项工作中，我们提出了一个增强学习框架，该框架从HMD和两个控制器中汲取了稀疏信号，并模拟了合理且物理上有效的全身运动。在训练期间，使用高质量的全身运动作为密集的监督，一个简单的政策网络可以学会为角色，步行和慢跑的角色输出适当的扭矩，同时紧随输入信号。我们的结果表明，即使输入仅是HMD的6D变换，也没有对下半身进行任何观察到令人惊讶的相似的腿部运动。我们还表明，单一政策可以对各种运动风格，不同的身体大小和新颖的环境具有牢固的态度。
Real-time tracking of human body motion is crucial for interactive and immersive experiences in AR/VR. However, very limited sensor data about the body is available from standalone wearable devices such as HMDs (Head Mounted Devices) or AR glasses. In this work, we present a reinforcement learning framework that takes in sparse signals from an HMD and two controllers, and simulates plausible and physically valid full body motions. Using high quality full body motion as dense supervision during training, a simple policy network can learn to output appropriate torques for the character to balance, walk, and jog, while closely following the input signals. Our results demonstrate surprisingly similar leg motions to ground truth without any observations of the lower body, even when the input is only the 6D transformations of the HMD. We also show that a single policy can be robust to diverse locomotion styles, different body sizes, and novel environments.
