Human pose estimation is a fundamental and appealing task in computer vision. 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 discerning distinct actions. 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 on EV-3DPW demonstrate that the robustness of our proposed 3D representation methods compared to traditional RGB images and event frame techniques under the same backbones. Our code and dataset have been made publicly available at https://github.com/MasterHow/EventPointPose.

翻译：人体姿态估计是计算机视觉中一项基础且富有吸引力的任务。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的场景下，其可靠性会显著下降。相比之下，事件相机为解决这些挑战性场景提供了鲁棒方案。主流方法通过将事件累积为事件帧，将事件相机融入学习框架。然而，这类方法往往忽视了事件固有的异步性和高时间分辨率特性，导致丢失了区分不同动作所需的关键时间维度信息。为解决此问题并挖掘事件信息的三维潜力，我们引入两种三维事件表征：栅格化事件点云（RasEPC）和解耦事件体素（DEV）。RasEPC 将同一位置的短时事件切片进行聚类，利用统计线索保留三维属性，显著降低了内存和计算需求。而 DEV 表征则将事件离散化为体素并投影至三个正交平面，通过解耦事件注意力从二维平面中检索三维线索。此外，我们开发并发布了 EV-3DPW，这是一个合成的事件数据集，旨在支持室外场景下的训练与定量分析。在公开真实世界数据集 DHP19 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实现了最高精度。在 EV-3DPW 上的实验表明，与基于相同骨干网络的传统 RGB 图像和事件帧技术相比，我们提出的三维表征方法具有更强的鲁棒性。我们的代码和数据集已开源至 https://github.com/MasterHow/EventPointPose。