用图神经网络处理事件相机数据，实现高频率单人姿态估计。
GraphEnet: Event-driven Human Pose Estimation with a Graph Neural Network
- 设计图网络结合线性事件表示，利用事件数据稀疏特性。
- 提出置信度池化与偏移向量学习，提升姿态估计精度。
- 首个将图神经网络用于事件相机人体姿态估计的工作，适合低功耗场景应用。
人体姿态估计是人机交互中的关键模块，随着深度学习技术的发展，基于RGB相机和商用GPU的鲁棒方法已广泛可用。然而，事件相机因其低延迟和低功耗优势，在资源受限场景（如便携设备和移动机器人）中日益受到关注。本文提出一种图神经网络模型GraphEnet，利用事件相机输出的稀疏特性，通过中间线状事件表示，实现高频率单人2D姿态估计。该架构引入新颖的偏移向量学习范式与基于置信度的池化机制，显著提升估计性能。这是首个将图神经网络应用于事件数据进行人体姿态估计的研究。代码已开源：https://github.com/event-driven-robotics/GraphEnet-NeVi-ICCV2025。
原文摘要 · Abstract (English)
Human Pose Estimation is a crucial module in human-machine interaction applications and, especially since the rise in deep learning technology, robust methods are available to consumers using RGB cameras and commercial GPUs. On the other hand, event-based cameras have gained popularity in the vision research community for their low latency and low energy advantages that make them ideal for applications where those resources are constrained like portable electronics and mobile robots. In this work we propose a Graph Neural Network, GraphEnet, that leverages the sparse nature of event camera output, with an intermediate line based event representation, to estimate 2D Human Pose of a single person at a high frequency. The architecture incorporates a novel offset vector learning paradigm with confidence based pooling to estimate the human pose. This is the first work that applies Graph Neural Networks to event data for Human Pose Estimation. The code is open-source at https://github.com/event-driven-robotics/GraphEnet-NeVi-ICCV2025.
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