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This report introduce our work on Egocentric 3D Hand Pose Estimationworkshop. Using AssemblyHands, this challenge focuses on egocentric 3D handpose estimation from a single-view image. In the competition, we adopt ViTbased backbones and a simple regressor for 3D keypoints prediction, whichprovides strong model baselines. We noticed that Hand-objects occlusions andself-occlusions lead to performance degradation, thus proposed a non-modelmethod to merge multi-view results in the post-process stage. Moreover, Weutilized test time augmentation and model ensemble to make further improvement.We also found that public dataset and rational preprocess are beneficial. Ourmethod achieved 12.21mm MPJPE on test dataset, achieve the first place inEgocentric 3D Hand Pose Estimation challenge.

1st Place Solution of Egocentric 3D Hand Pose Estimation Challenge 2023 Technical Report:A Concise Pipeline for Egocentric Hand Pose Reconstruction