综述事件相机在人脸与人体动作识别中的应用进展与挑战。
Event-based Solutions for Human-centered Applications: A Comprehensive Review
- 首次统一人体与面部任务的事件相机应用研究
- 涵盖事件压缩与仿真框架等关键支持技术
- 适合刚入门或想布局该领域的研究人员
事件相机（又称动态视觉传感器）能够异步捕捉光强变化，具备极高的时间分辨率和能效优势。这些特性使其特别适用于人机交互场景，可精准捕捉面部表情细节与人体复杂运动动态。尽管研究兴趣日益增长，但事件相机在人体与面部任务中的应用仍分散零落，缺乏系统性综述。本文首次整合两大领域，全面回顾技术进展、现存挑战与未来机遇，并深入探讨事件压缩与仿真框架等未被充分关注的方向，对推动事件相机在人中心应用中的普及具有重要意义。本综述旨在为新老研究者提供基础参考，明确当前研究状态并发现潜在突破方向。相关总结见 https://github.com/nmirabeth/event_human
原文摘要 · Abstract (English)
Event cameras, often referred to as dynamic vision sensors, are groundbreaking sensors capable of capturing changes in light intensity asynchronously, offering exceptional temporal resolution and energy efficiency. These attributes make them particularly suited for human-centered applications, as they capture both the most intricate details of facial expressions and the complex motion dynamics of the human body. Despite growing interest, research in human-centered applications of event cameras remains scattered, with no comprehensive overview encompassing both body and face tasks. This survey bridges that gap by being the first to unify these domains, presenting an extensive review of advancements, challenges, and opportunities. We also examine less-explored areas, including event compression techniques and simulation frameworks, which are essential for the broader adoption of event cameras. This survey is designed to serve as a foundational reference that helps both new and experienced researchers understand the current state of the field and identify promising directions for future work in human-centered event camera applications. A summary of this survey can be found at https://github.com/nmirabeth/event_human
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