  3D hand pose estimation and shape recovery are challenging tasks in computer
vision. We introduce a novel framework HandTailor, which combines a
learning-based hand module and an optimization-based tailor module to achieve
high-precision hand mesh recovery from a monocular RGB image. The proposed hand
module unifies perspective projection and weak perspective projection in a
single network towards accuracy-oriented and in-the-wild scenarios. The
proposed tailor module then utilizes the coarsely reconstructed mesh model
provided by the hand module as initialization, and iteratively optimizes an
energy function to obtain better results. The tailor module is time-efficient,
costs only 8ms per frame on a modern CPU. We demonstrate that HandTailor can
get state-of-the-art performance on several public benchmarks, with impressive
qualitative results on in-the-wild experiments.
