论文标题
轻量级3D人姿势估计网络使用教师学习
Lightweight 3D Human Pose Estimation Network Training Using Teacher-Student Learning
论文作者
论文摘要
我们提出Movnect，这是一种轻巧的深神经网络，可使用单个RGB摄像机捕获3D人姿势。为了提高模型的整体性能，我们将基于教师学习方法的知识蒸馏应用于3D人类姿势估计。实时后处理使CNN输出产生时间稳定的3D骨骼信息，可直接在应用程序中使用。我们实施了一个3D化身应用程序，实时在移动设备上运行，以证明我们的网络可以达到高精度和快速推理时间。广泛的评估表明，使用拟议的培训方法比以前的3D姿势估计方法在人类360万数据集和移动设备上使用的3D姿势估计方法的优势。
We present MoVNect, a lightweight deep neural network to capture 3D human pose using a single RGB camera. To improve the overall performance of the model, we apply the teacher-student learning method based knowledge distillation to 3D human pose estimation. Real-time post-processing makes the CNN output yield temporally stable 3D skeletal information, which can be used in applications directly. We implement a 3D avatar application running on mobile in real-time to demonstrate that our network achieves both high accuracy and fast inference time. Extensive evaluations show the advantages of our lightweight model with the proposed training method over previous 3D pose estimation methods on the Human3.6M dataset and mobile devices.
