mlsanityData to 7 Oct 2026

Structure-aware Editable Morphable Model for 3D Facial Detail Animation and Manipulation

arXiv 2207.09019 ↗First posted 19 Jul 2022cs.CV cs.GRcited by 4

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Abstract (arXiv)

Morphable models are essential for the statistical modeling of 3D faces. Previous works on morphable models mostly focus on large-scale facial geometry but ignore facial details. This paper augments morphable models in representing facial details by learning a Structure-aware Editable Morphable Model (SEMM). SEMM introduces a detail structure representation based on the distance field of wrinkle lines, jointly modeled with detail displacements to establish better correspondences and enable intuitive manipulation of wrinkle structure. Besides, SEMM introduces two transformation modules to translate expression blendshape weights and age values into changes in latent space, allowing effective semantic detail editing while maintaining identity. Extensive experiments demonstrate that the proposed model compactly represents facial details, outperforms previous methods in expression animation qualitatively and quantitatively, and achieves effective age editing and wrinkle line editing of facial details. Code and model are available at https://github.com/gerwang/facial-detail-manipulation.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 38 of its 86 references that are in the map. First posted on arXiv 19 Jul 2022.

Builds on

38 of 86 references.

References · 38

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26 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 1.

4 papers.

  1. 2023HiFace: High-Fidelity 3D Face Reconstruction by Learning Static and Dynamic Detailscited by 22
  2. 2026Deep Sketch-Based 3D Modeling: A Surveycited by 3
  3. 2025High-Quality Facial Albedo Generation for 3D Face Reconstruction from a Single Image using a Coarse-to-Fine Approachcited by 2
  4. 2024Facial Surgery Preview Based on the Orthognathic Treatment Predictioncited by 0