A Geometry Loss Combination for 3D Human Pose Estimation

Ai Matsune, Shichen Hu, Guangquan Li, Sihan Wen, Xiantan Zhu, Zhiming Tan; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, pp. 3272-3281

Root-relative loss has formed the basis of 3D human pose estimation for many years. However, this point-to-point loss treats every keypoint separately and ignores internal connection information of the human body. This leads to illegal pose prediction, which humans cannot form in the real world. It also suffers from differences in estimation difficulty between keypoints. The farther the keypoint is from the torso, the less accurate it is. To address the above problems, this paper proposes geometry loss combination to utilize the geometric relationship between each keypoint fully. This loss combination consists of three loss functions: root-relative pose, bone length, and body part orientation. The previous two have already been used in prior works. Beyond them, we further develop a loss function called body part orientation loss for local body parts. Intuitively, the human body can be divided into three parts: the head, torso, and limbs. Based on this, we select the corresponding keypoints and create virtual planes for each body part. Experiments with different datasets and models demonstrate that our proposed method improves the prediction accuracy. We also achieve MPJPE of 65.0 on the 3DPW test set, which outperforms state-of-the-art methods.