  We present a method for reconstructing accurate and consistent 3D hands from
a monocular video. We observe that detected 2D hand keypoints and the image
texture provide important cues about the geometry and texture of the 3D hand,
which can reduce or even eliminate the requirement on 3D hand annotation. Thus
we propose ${\rm {S}^{2}HAND}$, a self-supervised 3D hand reconstruction model,
that can jointly estimate pose, shape, texture, and the camera viewpoint from a
single RGB input through the supervision of easily accessible 2D detected
keypoints. We leverage the continuous hand motion information contained in the
unlabeled video data and propose ${\rm {S}^{2}HAND(V)}$, which uses a set of
weights shared ${\rm {S}^{2}HAND}$ to process each frame and exploits
additional motion, texture, and shape consistency constrains to promote more
accurate hand poses and more consistent shapes and textures. Experiments on
benchmark datasets demonstrate that our self-supervised approach produces
comparable hand reconstruction performance compared with the recent
full-supervised methods in single-frame as input setup, and notably improves
the reconstruction accuracy and consistency when using video training data.
