6

Figure 5. Visual comparison with state-of-the-art 3D face recon-
struction methods [48, 43, 24, 66, 15, 26, 65]. Results for related
methods are borrowed from [48].

the acquired reﬂectance, to re-render the textures under ar-
bitrary realistic environments, directly on the UV space:
ρ(AD, AS, N, S) → T. Although AvatarMe++ uses a sim-
ilar method to augment training data, we do not require this
process to be differentiable and use a ray-tracing renderer
[50] (Baker algorithm) to achieve more realistic textures.

To train our model, we use a KL-regularized latent diffu-
sion model with the default hyper-parameters proposed by
the authors of [57]. Speciﬁcally, we use a downsampling
factor of f = 8 for the perceptual auto-encoder and a diffu-
sion length of T = 1000 for the denoising model. We train
our model once and use it for texture and reﬂectance re-
construction from “in-the-wild” images. Below we provide
comprehensive qualitative and quantitative evaluations.
Relightify-Relightable3DFacesfromaSingleImageviaDiffusionModels