Corpus ID: 228083626

Portrait Neural Radiance Fields from a Single Image

@article{Gao2020PortraitNR,
  title={Portrait Neural Radiance Fields from a Single Image},
  author={Chen Gao and YiChang Shih and Wei-Sheng Lai and Chia-Kai Liang and J. Huang},
  journal={ArXiv},
  year={2020},
  volume={abs/2012.05903}
}
  • Chen Gao, YiChang Shih, +2 authors J. Huang
  • Published 2020
  • Computer Science
  • ArXiv
  • We present a method for estimating Neural Radiance Fields (NeRF) from a single headshot portrait. While NeRF has demonstrated high-quality view synthesis, it requires multiple images of static scenes and thus impractical for casual captures and moving subjects. In this work, we propose to pretrain the weights of a multilayer perceptron (MLP), which implicitly models the volumetric density and colors, with a meta-learning framework using a light stage portrait dataset. To improve the… CONTINUE READING
    2 Citations
    A-NeRF: Surface-free Human 3D Pose Refinement via Neural Rendering
    • PDF
    Neural Volume Rendering: NeRF And Beyond
    • 1
    • PDF

    References

    SHOWING 1-10 OF 70 REFERENCES
    NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
    • 130
    • Highly Influential
    • PDF
    Learning Perspective Undistortion of Portraits
    • 6
    • Highly Influential
    • PDF
    NeRF++: Analyzing and Improving Neural Radiance Fields
    • 13
    • PDF
    Deep 3D Portrait From a Single Image
    • S. Xu, J. Yang, +4 authors Xin Tong
    • Computer Science
    • 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
    • 2020
    • 6
    • Highly Influential
    • PDF
    Unsupervised Training for 3D Morphable Model Regression
    • 121
    • PDF
    Neural Light Transport for Relighting and View Synthesis
    • 4
    • PDF
    Unsupervised Learning of Probably Symmetric Deformable 3D Objects From Images in the Wild
    • 28
    • PDF
    HoloGAN: Unsupervised Learning of 3D Representations From Natural Images
    • 105
    • PDF
    Perspective-aware manipulation of portrait photos
    • 37
    • PDF