Human pose estimation is a critical component in autonomous driving and parking, enhancing safety by predicting human actions. Traditional frame-based cameras and videos are commonly applied, yet, they become less reliable in scenarios under high dynamic range or heavy motion blur. In contrast, event cameras offer a robust solution for navigating these challenging contexts. Predominant methodologies incorporate event cameras into learning frameworks by accumulating events into event frames. However, such methods tend to marginalize the intrinsic asynchronous and high temporal resolution characteristics of events. This disregard leads to a loss in essential temporal dimension data, crucial for safety-critical tasks associated with dynamic human activities. To address this issue and to unlock the 3D potential of event information, we introduce two 3D event representations: the Rasterized Event Point Cloud (RasEPC) and the Decoupled Event Voxel (DEV). The RasEPC collates events within concise temporal slices at identical positions, preserving 3D attributes with statistical cues and markedly mitigating memory and computational demands. Meanwhile, the DEV representation discretizes events into voxels and projects them across three orthogonal planes, utilizing decoupled event attention to retrieve 3D cues from the 2D planes. Furthermore, we develop and release EV-3DPW, a synthetic event-based dataset crafted to facilitate training and quantitative analysis in outdoor scenes. On the public real-world DHP19 dataset, our event point cloud technique excels in real-time mobile predictions, while the decoupled event voxel method achieves the highest accuracy. Experiments reveal our proposed 3D representation methods' superior generalization capacities against traditional RGB images and event frame techniques. Our code and dataset are available at https://github.com/MasterHow/EventPointPose.

翻译：人体姿态估算是自动驾驶和自动泊车领域的关键组成部分，通过预测人类行为来提升安全性。传统基于帧的相机和视频被广泛应用，但在高动态范围或严重运动模糊场景下其可靠性下降。相比之下，事件相机为应对这些具有挑战性的场景提供了鲁棒解决方案。主流方法通过将事件累积为事件帧的方式将事件相机融入学习框架，但此类方法往往削弱了事件固有的异步性和高时间分辨率特性。这种忽视导致关键的时间维度数据丢失，而这些数据对于涉及动态人类活动的安全攸关任务至关重要。为解决此问题并释放事件信息的三维潜力，我们引入两种3D事件表达：光栅化事件点云（RasEPC）和解耦事件体素（DEV）。RasEPC将同一位置紧凑时间片内的事件进行汇集，通过统计特征保留三维属性，显著降低内存和计算需求；而DEV表达则将事件离散化为体素并投影至三个正交平面，利用解耦事件注意力从二维平面中提取三维线索。此外，我们构建并公开了合成事件数据集EV-3DPW，旨在促进户外场景的训练与量化分析。在公开的真实世界DHP19数据集上，我们的事件点云技术在实时移动端预测中表现优异，而解耦事件体素方法则达到了最高精度。实验表明，我们提出的3D表达方法与传统RGB图像和事件帧技术相比具有更优的泛化能力。相关代码与数据集已发布于https://github.com/MasterHow/EventPointPose。