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REALY: Rethinking the Evaluation of 3D Face Reconstruction

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

The evaluation of 3D face reconstruction results typically relies on a rigid shape alignment between the estimated 3D model and the ground-truth scan. We observe that aligning two shapes with different reference points can largely affect the evaluation results. This poses difficulties for precisely diagnosing and improving a 3D face reconstruction method. In this paper, we propose a novel evaluation approach with a new benchmark REALY, consists of 100 globally aligned face scans with accurate facial keypoints, high-quality region masks, and topology-consistent meshes. Our approach performs region-wise shape alignment and leads to more accurate, bidirectional correspondences during computing the shape errors. The fine-grained, region-wise evaluation results provide us detailed understandings about the performance of state-of-the-art 3D face reconstruction methods. For example, our experiments on single-image based reconstruction methods reveal that DECA performs the best on nose regions, while GANFit performs better on cheek regions. Besides, a new and high-quality 3DMM basis, HIFI3D\(^{\pmb {+}\pmb {+}}\), is further derived using the same procedure as we construct REALY to align and retopologize several 3D face datasets. We will release REALY, HIFI3D\(^{\pmb {+}\pmb {+}}\), and our new evaluation pipeline at https://realy3dface.com.

Z. Chai and H. Zhang—Equal Contributions.

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Acknowledgment

This work was supported by SZSTC Grant No. JCYJ20190 809172201639 and WDZC20200820200655001, Shenzhen Key Laboratory ZDSY S20210623092001004.

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Authors and Affiliations

  1. Shenzhen International Graduate School, Tsinghua University, Beijing, China

    Zenghao Chai, Zhengzhuo Xu & Chun Yuan

  2. Tencent AI Lab, Bellevue, China

    Haoxian Zhang, Jing Ren, Di Kang, Xuefei Zhe & Linchao Bao

  3. Peng Cheng National Laboratory, Shenzhen, China

    Chun Yuan

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  1. Zenghao Chai
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  2. Haoxian Zhang
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  3. Jing Ren
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  4. Di Kang
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Correspondence to Chun Yuan or Linchao Bao .

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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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Chai, Z. et al. (2022). REALY: Rethinking the Evaluation of 3D Face Reconstruction. 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 13668. Springer, Cham. https://doi.org/10.1007/978-3-031-20074-8_5

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