3D gaze tracking from a single RGB camera is very challenging due to the lack of information in determining the accurate gaze target from a monocular RGB sequence. The eyes tend to occupy only a small portion of the video, and even small errors in estimated eye orientations can lead to very large errors in the triangulated gaze target. We overcome these difficulties with a novel lightweight eyeball calibration scheme that determines the user‐specific visual axis, eyeball size and position in the head. Unlike the previous calibration techniques, we do not need the ground truth positions of the gaze points. In the online stage, gaze is tracked by a new gaze fitting algorithm, and refined by a 3D gaze regression method to correct for bias errors. Our regression is pre‐trained on several individuals and works well for novel users. After the lightweight one‐time user calibration, our method operates in real time. Experiments show that our technique achieves state‐of‐the‐art accuracy in gaze angle estimation, and we demonstrate applications of 3D gaze target tracking and gaze retargeting to an animated 3D character.

中文翻译：

---

使用轻量级眼球校准进行准确的实时 3D 凝视跟踪

由于缺乏从单目 RGB 序列确定准确凝视目标的信息，因此从单个 RGB 相机进行 3D 凝视跟踪非常具有挑战性。眼睛往往只占视频的一小部分，即使估计的眼睛方向中的小错误也会导致三角化凝视目标中的非常大的错误。我们通过一种新颖的轻量级眼球校准方案克服了这些困难，该方案确定了用户特定的视轴、眼球大小和头部位置。与之前的校准技术不同，我们不需要注视点的真实位置。在在线阶段，视线通过新的视线拟合算法进行跟踪，并通过 3D 视线回归方法进行改进以纠正偏差错误。我们的回归对几个人进行了预训练，并且对新用户很有效。在轻量级一次性用户校准之后，我们的方法实时运行。实验表明，我们的技术在凝视角度估计方面达到了最先进的精度，并且我们展示了 3D 凝视目标跟踪和凝视重定向对动画 3D 角色的应用。