Efficient Multi-View 3D Human Pose Estimation via Knowledge Distillation

In this paper, we focus on the challenge that arises when deploying 3D HPE models in real-world scenarios where computational resources and memory constraints are prevalent. To address this challenge, our research focuses on leveraging knowledge distillation as a technique to reduce the model’s complexity and computational demands while preserving its performance. Knowledge distillation involves transferring the knowledge acquired by a complex teacher model to a more lightweight student model. By distilling the essential information, we aim to create a streamlined and efficient 3D HPE model capable of maintaining high accuracy with reduced computational overhead. By training the Student Model with different types of ResNet model, i.e., ResNet-50, ResNet-101, we get the result that with the rate of declining parameters from Teacher Model to Student Model reaching 41% for ResNet-50 and 73% for ResNet-101, it’s able to conclude we address the challenge of deploying 3D HPE systems in resource-constrained real-world settings. Ultimately, we aspire to empower researchers and practitioners with adaptable, efficient tools that transcend the boundaries of 3D pose estimation, shaping the landscape of computer vision applications.