4. Mapping Shape Representations

In Sec. 3 we introduce three body-shape representations:
(1) SMPL-X’s PCA shape space (Sec. 3.1), (2) anthropo-
metric measurements (Sec. 3.2), and (3) linguistic shape at-
tribute scores (Sec. 3.3). Here we learn mappings between
these, so that in Sec. 5 we can define new losses for training
body shape regressors using multiple data sources.
4.1. Virtual Measurements (VM)

We obtain anthropometric measurements from a 3D
body mesh in a T-pose, namely height, H(β), weight,
W (β), and chest, waist and hip circumferences, Cc(β),
Cw(β), and Ch(β), respectively, by following Wuhrer
et al. [63] and the “Virtual Caliper” [46]. For details on
how we compute these measurements, see Sup. Mat.

K(cid:88)

k=1

1
K

4.2. Attributes and 3D Shape
Accurate 3D Body Shape Regression using Metric and Semantic Attributes