We also compute normals in canonical space. The nor-
mal n at a certain canonical point x is computed as the spa-
tial gradient of the signed distance function at that point:

n = ∇xd(x, z, p).

(2)

The canonical appearance is represented by a 3D texture
ﬁeld c. We also predict features f that are used to guide the
super-resolution module (described later). We denote the
entire mapping from 3D point x, latent vector z and pose
condition p to SDF d, normal n, color c and color features
f in canonical space as follows:

g : R3 × Rnz × Rnp → R × R3 × R3 × Rnf

(3)

(x, z, p) (cid:55)→ (d, n, c, f ).
AG3D- Learning to Generate 3D Avatars from 2D Image Collections