  In this paper, we consider a novel problem of reconstructing a 3D human
avatar from multiple unconstrained frames, independent of assumptions on camera
calibration, capture space, and constrained actions. The problem should be
addressed by a framework that takes multiple unconstrained images as inputs,
and generates a shape-with-skinning avatar in the canonical space, finished in
one feed-forward pass. To this end, we present 3D Avatar Reconstruction in the
wild (ARwild), which first reconstructs the implicit skinning fields in a
multi-level manner, by which the image features from multiple images are
aligned and integrated to estimate a pixel-aligned implicit function that
represents the clothed shape. To enable the training and testing of the new
framework, we contribute a large-scale dataset, MVP-Human (Multi-View and
multi-Pose 3D Human), which contains 400 subjects, each of which has 15 scans
in different poses and 8-view images for each pose, providing 6,000 3D scans
and 48,000 images in total. Overall, benefits from the specific network
architecture and the diverse data, the trained model enables 3D avatar
reconstruction from unconstrained frames and achieves state-of-the-art
performance.
