  From an image of a person, we can easily infer the natural 3D pose and shape
of the person even if ambiguity exists. This is because we have a mental model
that allows us to imagine a person's appearance at different viewing directions
from a given image and utilize the consistency between them for inference.
However, existing human mesh recovery methods only consider the direction in
which the image was taken due to their structural limitations. Hence, we
propose "Implicit 3D Human Mesh Recovery (ImpHMR)" that can implicitly imagine
a person in 3D space at the feature-level via Neural Feature Fields. In ImpHMR,
feature fields are generated by CNN-based image encoder for a given image.
Then, the 2D feature map is volume-rendered from the feature field for a given
viewing direction, and the pose and shape parameters are regressed from the
feature. To utilize consistency with pose and shape from unseen-view, if there
are 3D labels, the model predicts results including the silhouette from an
arbitrary direction and makes it equal to the rotated ground-truth. In the case
of only 2D labels, we perform self-supervised learning through the constraint
that the pose and shape parameters inferred from different directions should be
the same. Extensive evaluations show the efficacy of the proposed method.
