3. Representations & Data for Body Shape

We use linguistic shape attributes and anthropometric
measurements as a connecting component between in-the-
wild images and ground-truth body shapes; see Fig. 4. To
that end, we annotate linguistic shape attributes for 3D
meshes and in-the-wild images, the latter from fashion-
model agencies, labeled via Amazon Mechanical Turk.

Figure 5. Histogram of height and chest/waist/hips circumference
for data from model-agency websites (Sec. 3.2) and CAESAR.
Model-agency data is diverse, yet not as much as CAESAR data.

3.1. SMPL-X Body Model

We use SMPL-X [43], a differentiable model that maps
shape, β, pose, θ, and expression, ψ, parameters to a 3D
mesh, M, with N = 10, 475 vertices, V . The shape vector
β ∈ RB (B ≤ 300) has coefficients of a low-dimensional
PCA space. The vertices are posed with linear blend skin-
ning with a learned rigged skeleton, X ∈ R55×3.
3.2. Model-Agency Images

and

anthropometric measurements
Accurate 3D Body Shape Regression using Metric and Semantic Attributes