mlsanityData to 14 Oct 2026

MoRF: Mobile Realistic Fullbody Avatars from a Monocular Video

arXiv 2303.10275 ↗First posted 17 Mar 2023cs.CVcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 65 of its 74 references that are in the map. First posted on arXiv 17 Mar 2023.

Builds on

65 of 74 references.

References · 65

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38 more
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50 most cited of 65 All 65 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 1; 2026: 3.

7 papers.

  1. 2024PhysAvatar: Learning the Physics of Dressed 3D Avatars from Visual Observationscited by 51
  2. 2024HAHA: Highly Articulated Gaussian Human Avatars with Textured Mesh Priorcited by 31
  3. 2024SqueezeMe: Mobile-Ready Distillation of Gaussian Full-Body Avatarscited by 11
  4. 2026High-Fidelity Mobile Avatars with Pruned Local Blendshapescited by 2
  5. 2025Frequency-Decomposed Avatar Representation for Varying Camera Distancescited by 1
  6. 2026NBAvatar: Neural Billboards Avatars with Realistic Hand-Face Interactioncited by 1
  7. 2026MUA: Mobile Ultra-detailed Animatable Avatarscited by 0