JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015

A-pose. IP-Net [57] jointly predict the outer 3D surface of
a human model, the inner body surface and the body part
labels, but is restricted to point cloud inputs. In contrast,
our method can naturally handle human-object interaction
and can be ﬂexibly adopted in both single-image and multi-
image setups.

3 SURFACE REPRESENTATION
3.1 Deep Implicit Function
A deep implicit function deﬁnes a surface as the level set
of an occupancy probability function F , e.g. F (p) = 0.5,
where p ∈ R3 denotes a 3D point and F : R3 (cid:55)→ [0, 1] is
represented by a deep neural network. In practice, in order
to represent the surface of a speciﬁc object using a general
neural network, the function F also takes a condition vari-
able (e.g., image feature of the object) as input and thus can
be written as:

F (p, c) : R3 × X (cid:55)→ [0, 1]
PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction