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Single depth image 3D face reconstruction via domain adaptive learning

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References

  1. Zhong Y, Pei Y, Li P, Guo Y, Ma G, Liu M, Bai W, Wu W, Zha H. Face denoising and 3D reconstruction from a single depth image. In: Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition. 2020, 117–124

  2. Deng Y, Yang J, Xu S, Chen D, Jia Y, Tong X. Accurate 3D face reconstruction with weakly-supervised learning: from single image to image set. In: Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 2019, 285–295

  3. Guo J, Zhu X, Yang Y, Yang F, Lei Z, Li S Z. Towards fast, accurate and stable 3D dense face alignment. In: Proceedings of the 16th European Conference on Computer Vision. 2020, 152–168

  4. Zielonka W, Bolkart T, Thies J. Towards metrical reconstruction of human faces. In: Proceedings of the 17th European Conference on Computer Vision. 2022, 250–269

  5. Lei B, Ren J, Feng M, Cui M, Xie X. A hierarchical representation network for accurate and detailed face reconstruction from in-the-wild images. In: Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023, 394–403

  6. Cao Z, Chu Z, Liu D, Chen Y. A vector-based representation to enhance head pose estimation. In: Proceedings of IEEE/CVF Winter Conference on Applications of Computer Vision. 2021, 1187–1196

  7. Meyer G P, Gupta S, Frosio I, Reddy D, Kautz J. Robust model-based 3D head pose estimation. In: Proceedings of IEEE International Conference on Computer Vision. 2015, 3649–3657

  8. Borghi G, Venturelli M, Vezzani R, Cucchiara R. POSEidon: face-from-depth for driver pose estimation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2017, 5494–5503

  9. Xin M, Mo S, Lin Y. EVA-GCN: head pose estimation based on graph convolutional networks. In: Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021, 1462–1471

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Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University, Hangzhou, 310013, China

    Xiaoxu Cai, Jianwen Lou, Jiajun Bu & Haishuai Wang

  2. School of Creative Technologies, University of Portsmouth, Portsmouth, PO1 2DJ, UK

    Xiaoxu Cai & Hui Yu

  3. College of Computer Science and Technology, Ocean University of China, Qingdao, 266100, China

    Junyu Dong

Authors
  1. Xiaoxu Cai
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  2. Jianwen Lou
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  3. Jiajun Bu
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  4. Junyu Dong
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  5. Haishuai Wang
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  6. Hui Yu
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Corresponding authors

Correspondence to Xiaoxu Cai or Jianwen Lou.

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Competing interests The authors declare that they have no competing interests or financial conflicts to disclose.

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Supporting information The supporting information is available online at journal.hep.com.cn and link.springer.com.

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Cai, X., Lou, J., Bu, J. et al. Single depth image 3D face reconstruction via domain adaptive learning. Front. Comput. Sci. 18, 181342 (2024). https://doi.org/10.1007/s11704-023-3541-7

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  • DOI: https://doi.org/10.1007/s11704-023-3541-7

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