  The latest trends in the research field of single-view human reconstruction
devote to learning deep implicit functions constrained by explicit body shape
priors. Despite the remarkable performance improvements compared with
traditional processing pipelines, existing learning approaches still show
different aspects of limitations in terms of flexibility, generalizability,
robustness, and/or representation capability. To comprehensively address the
above issues, in this paper, we investigate an explicit point-based human
reconstruction framework called HaP, which adopts point clouds as the
intermediate representation of the target geometric structure. Technically, our
approach is featured by fully-explicit point cloud estimation, manipulation,
generation, and refinement in the 3D geometric space, instead of an implicit
learning process that can be ambiguous and less controllable. The overall
workflow is carefully organized with dedicated designs of the corresponding
specialized learning components as well as processing procedures. Extensive
experiments demonstrate that our framework achieves quantitative performance
improvements of 20% to 40% over current state-of-the-art methods, and better
qualitative results. Our promising results may indicate a paradigm rollback to
the fully-explicit and geometry-centric algorithm design, which enables to
exploit various powerful point cloud modeling architectures and processing
techniques. We will make our code and data publicly available at
https://github.com/yztang4/HaP.
