T (β, θ) = T + Bs(β) + Bp(θ)

(4)
where linear blend-skinning W (·) with skinning weights
W poses the T-pose template T + Bs(β) + Bp(θ) based
on its skeleton joints J(·). As shown in DeepHuman, a
SMPL model ﬁtted to the input image can serve as a
strong geometry prior, and guarantee pose and shape aware
human model reconstructions. However, estimating SMPL
model given a single image is a fundamentally ill-posed
problem. Recent studies have explored to learn from large-
scale datasets to address this challenge [11], [12], [13], [14],
[15], [37], [38], [39], [40]. In this work we use GCMR [13],
one of the state-of-the-art networks as the backbone for body
shape inference. Note that GCMR can be replaced with other
equivalent networks such as SPIN [38] for single images and
VIBE [39] for videos, as we do not make any assumptions on
how the initial body is estimated and our method is able to
deal with inaccurate body initialization thanks to our body
reference optimization (Sec.4.3).

(2)
PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction