pancy at point P, denoted as(cid:98)o(P). A mean squared error

4. Experiments
4.1. Baseline models

We compare ICON primarily with PIFu [54] and
PaMIR [70]. These methods differ from ICON and from
each other w.r.t. the training data, the loss functions, the
network structure, the use of the SMPL body prior, etc. To
isolate and evaluate each factor, we re-implement PIFu and
PaMIR by “simulating” them based on ICON’s architecture.
This provides a uniﬁed benchmarking framework, and en-
ables us to easily train each baseline with the exact same data
and training hyper-parameters for a fair comparison. Since
there might be small differences w.r.t. the original models,
we denote the “simulated” models with a “star” as:

: {f2D(I,N )}

→ O,
• PIFu∗
• PaMIR∗ : {f2D(I,N ), f3D(V)} → O,
• ICON : {N , γ(M)}
→ O,
ICON