用上身姿态和深度图实现无脸3D注视目标检测，保护隐私。
Upper-Body Pose-based Gaze Estimation for Privacy-Preserving 3D Gaze Target Detection
- 基于上身姿态与深度图推断3D注视方向
- 在无面部图像条件下达到顶尖性能
- 适合注重隐私的监控与人机交互场景
注视目标检测（GTD）是从外部视角确定一个人在三维空间中注视位置的挑战性任务。现有方法主要依赖于分析个人外观，尤其是面部信息来预测注视目标。本文提出一种新方法，利用上身姿态和可用的深度图提取3D注视方向，并通过多阶段或端到端流程预测注视目标。准确的躯干姿态可提供头部姿态信息，从而近似注视方向，同时反映手臂与手的位置，关联其行为及可能关注的物体。因此，除了实现3D注视估计外，还可同步完成GTD。我们在最全面的公开3D注视目标检测数据集上取得了当前最优结果，且无需人脸图像，显著提升各类应用中的隐私保护能力。代码已开源：https://github.com/intelligolabs/privacy-gtd-3D。
原文摘要 · Abstract (English)
Gaze Target Detection (GTD), i.e., determining where a person is looking within a scene from an external viewpoint, is a challenging task, particularly in 3D space. Existing approaches heavily rely on analyzing the person's appearance, primarily focusing on their face to predict the gaze target. This paper presents a novel approach to tackle this problem by utilizing the person's upper-body pose and available depth maps to extract a 3D gaze direction and employing a multi-stage or an end-to-end pipeline to predict the gazed target. When predicted accurately, the human body pose can provide valuable information about the head pose, which is a good approximation of the gaze direction, as well as the position of the arms and hands, which are linked to the activity the person is performing and the objects they are likely focusing on. Consequently, in addition to performing gaze estimation in 3D, we are also able to perform GTD simultaneously. We demonstrate state-of-the-art results on the most comprehensive publicly accessible 3D gaze target detection dataset without requiring images of the person's face, thus promoting privacy preservation in various application contexts. The code is available at https://github.com/intelligolabs/privacy-gtd-3D.
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