Head pose estimation is one of the essential tasks in computer vision, which predicts the Euler angles of the head in an image. In recent years, CNN-based methods for head pose estimation have achieved excellent performance. Their training relies on RGB images providing facial landmarks or depth images from RGBD cameras. However, labeling facial landmarks is complex for large angular head poses in RGB images, and RGBD cameras are unsuitable for outdoor scenes. We propose a simple, effective, and convenient method for annotating the Euler angles of head poses in RGB images. The method novely uses a 3D virtual human head to simulate the head pose in the RGB image. The Euler angle can be calculated from the change in coordinates of the 3D virtual head. We then create a dataset using our annotation method: 2DHeadPose dataset, which contains a rich set of attributes, dimensions, and angles. Finally, we propose a label smoothing method to suppress the noise introduced by the head pose annotation process and establish a baseline approach. Experiments demonstrate that our annotation method, datasets, and label smoothing are very effective. Our baseline approach surpasses most current state-of-the-art methods. The annotation tool, dataset, and source code are publicly available at https://github.com/youngnuaa/2DHeadPose .
The dataset baidu download path：https://pan.baidu.com/s/1CZPqyOMMCKNmV9uSLg8QNA?pwd=581p code:581p
The dataset google download path：https://drive.google.com/file/d/14BGFd8zrz-e1xaRxMQvgnUU3FV41pMMm/view?usp=share_link