  Recent progress in human shape learning, shows that neural implicit models
are effective in generating 3D human surfaces from limited number of views, and
even from a single RGB image. However, existing monocular approaches still
struggle to recover fine geometric details such as face, hands or cloth
wrinkles. They are also easily prone to depth ambiguities that result in
distorted geometries along the camera optical axis. In this paper, we explore
the benefits of incorporating depth observations in the reconstruction process
by introducing ANIM, a novel method that reconstructs arbitrary 3D human shapes
from single-view RGB-D images with an unprecedented level of accuracy. Our
model learns geometric details from both multi-resolution pixel-aligned and
voxel-aligned features to leverage depth information and enable spatial
relationships, mitigating depth ambiguities. We further enhance the quality of
the reconstructed shape by introducing a depth-supervision strategy, which
improves the accuracy of the signed distance field estimation of points that
lie on the reconstructed surface. Experiments demonstrate that ANIM outperforms
state-of-the-art works that use RGB, surface normals, point cloud or RGB-D data
as input. In addition, we introduce ANIM-Real, a new multi-modal dataset
comprising high-quality scans paired with consumer-grade RGB-D camera, and our
protocol to fine-tune ANIM, enabling high-quality reconstruction from
real-world human capture.
