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This report describes our 1st place solution to ECCV 2022 challenge on HumanBody, Hands, and Activities (HBHA) from Egocentric and Multi-view Cameras (handpose estimation). In this challenge, we aim to estimate global 3D hand posesfrom the input image where two hands and an object are interacting on theegocentric viewpoint. Our proposed method performs end-to-end multi-hand poseestimation via transformer architecture. In particular, our method robustlyestimates hand poses in a scenario where two hands interact. Additionally, wepropose an algorithm that considers hand scales to robustly estimate theabsolute depth. The proposed algorithm works well even when the hand sizes arevarious for each person. Our method attains 14.4 mm (left) and 15.9 mm (right)errors for each hand in the test set.

Transformer-based Global 3D Hand Pose Estimation in Two Hands Manipulating Objects Scenarios