6. Conclusion

In this paper we introduced Relightify, a method that
achieves state-of-the-art facial texture completion and facial
reﬂectance acquisition, from monocular “in-the-wild” im-
ages. To achieve this, we train a latent diffusion model with
multiple encoder-decoder networks, on a synthetic facial
texture and reﬂectance dataset, and use a diffusion-based
inpainting method on the masked UV textures. Our results
directly acquire the visible facial parts while also extrapo-
lating to facial reﬂectance that exhibits a high likeness to
the input image and can be trivially employed in commer-
cial rendering applications.
Acknowledgements.
Stefanos Zafeiriou acknowledges
support from the EPSRC Fellowship DEFORM: Large
Scale Shape Analysis of Deformable Models of Humans
(EP/S010203/1).

8

InputPartial UVScore-SDERePaintMCGMCG + DDIMReferences
[1] https://github.com/ascust/

3DMM-Fitting-Pytorch.
Relightify-Relightable3DFacesfromaSingleImageviaDiffusionModels