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 coarseto-
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. The codes will be released in
https://github.com/TingtingLiao/CAR.