  We present DINAR, an approach for creating realistic rigged fullbody avatars
from single RGB images. Similarly to previous works, our method uses neural
textures combined with the SMPL-X body model to achieve photo-realistic quality
of avatars while keeping them easy to animate and fast to infer. To restore the
texture, we use a latent diffusion model and show how such model can be trained
in the neural texture space. The use of the diffusion model allows us to
realistically reconstruct large unseen regions such as the back of a person
given the frontal view. The models in our pipeline are trained using 2D images
and videos only. In the experiments, our approach achieves state-of-the-art
rendering quality and good generalization to new poses and viewpoints. In
particular, the approach improves state-of-the-art on the SnapshotPeople public
benchmark.
