MoRF: Mobile Realistic Fullbody Avatars from a Monocular Video
This addresses the need for efficient and high-quality avatar generation for mobile applications, though it is incremental over existing methods.
The paper tackles the problem of creating realistic full-body avatars from monocular videos that can run in real-time on mobile devices, achieving higher image sharpness and temporal consistency compared to prior systems, with user study participants preferring MoRF avatars.
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 per-frame 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.