  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 practical direction. However, existing multi-view hand pose
estimation methods suffer from two main drawbacks: 1) Requiring multi-view
annotations for training, which are expensive. 2) During testing, the model
becomes inapplicable 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 pre-trained 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
pair-wise cross-view consensus and invariance of transformation between both
views. These two stereo constraints are used in a complementary manner to
generate pseudo-labels, allowing reliable adaptation. Evaluation 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/MickeyLLG/S2DHand.
