Fine Structure-Aware Sampling: A New Sampling Training Scheme for
  Pixel-Aligned Implicit Models in Single-View Human Reconstruction
Kennard Yanting Chan Fayao Liu Guosheng Lin Chuan Sheng Foo Weisi Lin
Pixel-aligned implicit models, such as PIFu, PIFuHD, and ICON, are used for
single-view clothed human reconstruction. These models need to be trained using
a sampling training scheme. Existing sampling training schemes either fail to
capture thin surfaces (e.g. ears, fingers) or cause noisy artefacts in
reconstructed meshes. To address these problems, we introduce Fine
Structured-Aware Sampling (FSS), a new sampling training scheme to train
pixel-aligned implicit models for single-view human reconstruction. FSS
resolves the aforementioned problems by proactively adapting to the thickness
and complexity of surfaces. In addition, unlike existing sampling training
schemes, FSS shows how normals of sample points can be capitalized in the
training process to improve results. Lastly, to further improve the training
process, FSS proposes a mesh thickness loss signal for pixel-aligned implicit
models. It becomes computationally feasible to introduce this loss once a
slight reworking of the pixel-aligned implicit function framework is carried
out. Our results show that our methods significantly outperform SOTA methods
qualitatively and quantitatively. Our code is publicly available at
https://github.com/kcyt/FSS.