L = Ex0,(cid:15),t

µθ(xt, t) =

1√
αt

(cid:15)θ(xt, t)

(cid:19)

(5)

In this work, we harness the power of diffusion models to
learn a strong generative prior over the domain of facial tex-
ture/reﬂectance. In particular, we adopt a physically-based
perspective by separating the facial reﬂectance into differ-
ent UV maps, namely diffuse albedo (Ad), specular albedo
(As) and surface normals (N) with high-frequency details.
This allows realistic rendering under different illumination
conditions. We learn our prior using a high-quality dataset
consisting of complete pairs of facial reﬂectance, and a cor-
responding rendered texture T under arbitrary illumination.
More details on the data we use are provided in section 4.1.
We train an unconditional diffusion model (as described in
section 3.1) on the quadruples:

x = [T, Ad, As, N] ∈ R512×512×12
Relightify-Relightable3DFacesfromaSingleImageviaDiffusionModels