MoRF: Mobile Realistic Fullbody Avatars from a Monocular Video

Renat Bashirov, Alexey Larionov, Evgeniya Ustinova, Mikhail Sidorenko, David Svitov, Ilya Zakharkin, Victor Lempitsky

Abstract: We present a system to create Mobile Realistic Fullbody (MoRF) avatars. MoRF avatars are rendered in real-time on mobile devices, learned from monocular videos, and have high realism. We use SMPL-X as a proxy geometry and render it with DNR (neural texture and image-2-image network). We improve on prior work, by overfitting perframe warping fields in the neural texture space, allowing to better align the training signal between different frames. We also refine SMPL-X mesh fitting procedure to improve the overall avatar quality. In the comparisons to other monocular video-based avatar systems, MoRF avatars achieve higher image sharpness and temporal consistency. Participants of our user study also preferred avatars generated by MoRF.