In contrast to previous work, SHAPY is trained with
in-the-wild images paired with linguistic shape attributes,
which are annotations that can be easily crowd-sourced for
weak shape supervision. We also go beyond SSP-3D to pro-
vide HBW, a new dataset with in-the-wild images, varied
clothing, and precise GT from 3D scans.

Shape, measurements and attributes: Body shapes
can be generated from anthropometric measurements [2,
54, 55]. Tsoli et al. [58] register a body model to multiple
high-resolution body scans to extract body measurements.
The “Virtual Caliper” [46] allows users to build metrically
accurate avatars of themselves using measurements or VR
game controllers. ViBE [21] collects images, measurements
(bust, waist, hip circumference, height) and the dress-size
of models from clothing websites to train a clothing rec-
ommendation network. We draw inspiration from these ap-
proaches for data collection and supervision.
Accurate 3D Body Shape Regression using Metric and Semantic Attributes