Late Breaking Results: A real-time diffusion-based filter for human pose estimation on edge devices

Human Pose Estimation (HPE) is increasingly being adopted in a wide range of applications, from healthcare to In- dustry 5.0. To address the intrinsic inaccuracy of such CNN-based software, the current trend involves applying filtering models to refine and improve the inference results. However, state-of-the- art filtering models are computationally intensive, limiting their use in resource-constrained devices. To overcome this limitation, we propose a real-time filtering technique based on diffusion models designed specifically for edge devices. Through a micro- benchmarking phase, we analyze how the model responds to various levels of noise and select the optimal setup for specific application scenarios. Using a widely available edge device, we evaluated the model’s performance on both synthetic and real noise generated by a state-of-the-art HPE system. Preliminary results demonstrate a significant improvement in real-time filter- ing performance with minimal computational overhead.