Single-to-Dual-View Adaptation for Egocentric 3D Hand Pose Estimation

Abstract

The pursuit of accurate 3D hand pose estimation stands as a keystone for understanding human activity in the realm of egocentric vision. The majority of existing estimation methods still rely on single-view images as input, leading to potential limitations, e.g., limited field-of-view and ambiguity in depth. To address these problems, adding another camera to better capture the shape of hands is a practi-cal direction. However, existing multi-view hand pose estimation methods suffer from two main drawbacks: 1) Re-quiring multi-view annotations for training, which are ex-pensive. 2) During testing, the model becomes inapplica-ble if camera parameters/layout are not the same as those used in training. In this paper, we propose a novel Single-to-Dual-view adaptation (S2DHand) solution that adapts a pretrained single-view estimator to dual views. Compared with existing multi-view training methods, 1) our adaptation process is unsupervised, eliminating the need for multi-view annotation. 2) Moreover, our method can handle arbitrary dual-view pairs with unknown camera parameters, making the model applicable to diverse camera settings. Specifically, S2DHand is built on certain stereo constraints, including pairwise cross-view consensus and invariance of transformation between both views. These two stereo constraints are used in a complementary manner to gen-erate pseudo-labels, allowing reliable adaptation. Evalu-ation results reveal that S2DHand achieves significant improvements on arbitrary camera pairs under both in-dataset and cross-dataset settings, and outperforms existing adaptation methods with leading performance. Project page: https://github.com/ut-vision/S2DHand.