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

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

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.

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Acknowledgements

This work was supported by Beijing Natural Science Foundation (JQ19015), the NSFC (No. 62021002, 61727808), the National Key R &D Program of China 2018YFA0704000, the Key Research and Development Project of Tibet Autonomous Region (XZ202101ZY0019G). This work was supported by THUIBCS, Tsinghua University and BLBCI, Beijing Municipal Education Commission. Feng Xu is the corresponding author.

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Authors and Affiliations

  1. BNRist and School of Software, Tsinghua University, Beijing, China

    Jingwang Ling, Zhibo Wang & Feng Xu

  2. Intel Labs, Beijing, China

    Ming Lu

  3. Sensetime Research, Shanghai, China

    Quan Wang & Chen Qian

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  1. Jingwang Ling
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Correspondence to Feng Xu .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Ling, J., Wang, Z., Lu, M., Wang, Q., Qian, C., Xu, F. (2022). Structure-Aware Editable Morphable Model for 3D Facial Detail Animation and Manipulation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13663. Springer, Cham. https://doi.org/10.1007/978-3-031-20062-5_15

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