MoRF: Mobile Realistic Fullbody Avatars from a Monocular Video

We present a new approach for learning Mobile Realistic Fullbody (MoRF) avatars. MoRF avatars can be rendered in real-time on mobile phones, have high realism, and can be learned from monocular videos. As in previous works, we use a combination of neural textures and the mesh-based body geometry modeling SMPL-X. We improve on prior work, by learning per-frame warping fields in the neural texture space, allowing to better align the training signal between different frames. We also apply existing SMPL-X fitting procedure refinements for videos to improve 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.