  This paper presents a framework for efficient 3D clothed avatar
reconstruction. By combining the advantages of the high accuracy of
optimization-based methods and the efficiency of learning-based methods, we
propose a coarse-to-fine way to realize a high-fidelity clothed avatar
reconstruction (CAR) from a single image. At the first stage, we use an
implicit model to learn the general shape in the canonical space of a person in
a learning-based way, and at the second stage, we refine the surface detail by
estimating the non-rigid deformation in the posed space in an optimization way.
A hyper-network is utilized to generate a good initialization so that the
convergence o f the optimization process is greatly accelerated. Extensive
experiments on various datasets show that the proposed CAR successfully
produces high-fidelity avatars for arbitrarily clothed humans in real scenes.
