  Gait recognition, which identifies individuals based on their walking
patterns, is an important biometric technique since it can be observed from a
distance and does not require the subject's cooperation. Recognizing a person's
gait is difficult because of the appearance variants in human silhouette
sequences produced by varying viewing angles, carrying objects, and clothing.
Recent research has produced a number of ways for coping with these variants.
In this paper, we present the usage of inferring 3-D body shapes distilled from
limited images, which are, in principle, invariant to the specified variants.
Inference of 3-D shape is a difficult task, especially when only silhouettes
are provided in a dataset. We provide a method for learning 3-D body inference
from silhouettes by transferring knowledge from 3-D shape prior from RGB
photos. We use our method on multiple existing state-of-the-art gait baselines
and obtain consistent improvements for gait identification on two public
datasets, CASIA-B and OUMVLP, on several variants and settings, including a new
setting of novel views not seen during training.
