This report presents our team's 'PCIE_EgoHandPose' solution for the EgoExo4D
Hand Pose Challenge at CVPR2024. The main goal of the challenge is to
accurately estimate hand poses, which involve 21 3D joints, using an RGB
egocentric video image provided for the task. This task is particularly
challenging due to the subtle movements and occlusions. To handle the
complexity of the task, we propose the Hand Pose Vision Transformer (HP-ViT).
The HP-ViT comprises a ViT backbone and transformer head to estimate joint
positions in 3D, utilizing MPJPE and RLE loss function. Our approach achieved
the 1st position in the Hand Pose challenge with 25.51 MPJPE and 8.49 PA-MPJPE.
Code is available at this https URL

我们的研究小组在 CVPR2024 的 EgoExo4D Hand Pose Challenge 中，通过我们的 'PCIE_EgoHandPose' 解决方案成功估计了包含 21 个三维关节的手部姿态，所提出的 Hand Pose Vision Transformer (HP-ViT) 结合了 ViT 骨干和变换器头来预测三维关节位置，采用 MPJPE 和 RLE 损失函数，在 Hand Pose 挑战中获得第一名，MPJPE 为 25.51，PA-MPJPE 为 8.49。