  We address the problem of clothed human reconstruction from a single image or
uncalibrated multi-view images. Existing methods struggle with reconstructing
detailed geometry of a clothed human and often require a calibrated setting for
multi-view reconstruction. We propose a flexible framework which, by leveraging
the parametric SMPL-X model, can take an arbitrary number of input images to
reconstruct a clothed human model under an uncalibrated setting. At the core of
our framework is our novel self-evolved signed distance field (SeSDF) module
which allows the framework to learn to deform the signed distance field (SDF)
derived from the fitted SMPL-X model, such that detailed geometry reflecting
the actual clothed human can be encoded for better reconstruction. Besides, we
propose a simple method for self-calibration of multi-view images via the
fitted SMPL-X parameters. This lifts the requirement of tedious manual
calibration and largely increases the flexibility of our method. Further, we
introduce an effective occlusion-aware feature fusion strategy to account for
the most useful features to reconstruct the human model. We thoroughly evaluate
our framework on public benchmarks, demonstrating significant superiority over
the state-of-the-arts both qualitatively and quantitatively.
