Learning to Reconstruct People in Clothing from a Single RGB Camera

@article{Alldieck2019LearningTR,
  title={Learning to Reconstruct People in Clothing from a Single RGB Camera},
  author={Thiemo Alldieck and Marcus A. Magnor and Bharat Lal Bhatnagar and Christian Theobalt and Gerard Pons-Moll},
  journal={ArXiv},
  year={2019},
  volume={abs/1903.05885}
}
We present a learning-based model to infer the personalized 3D shape of people from a few frames (1-8) of a monocular video in which the person is moving, in less than 10 seconds with a reconstruction accuracy of 5mm. Our model learns to predict the parameters of a statistical body model and instance displacements that add clothing and hair to the shape. The model achieves fast and accurate predictions based on two key design choices. First, by predicting shape in a canonical T-pose space, the… CONTINUE READING
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Multi-Garment Net: Learning to Dress 3D People from Images

Bharat Lal Bhatnagar, Garvita Tiwari, Christian Theobalt, Gerard Pons-Moll
  • ArXiv
  • 2019
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References

Publications referenced by this paper.
SHOWING 1-10 OF 86 REFERENCES

Video Based Reconstruction of 3D People Models

  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
  • 2018
VIEW 10 EXCERPTS

Learning to Estimate 3D Human Pose and Shape from a Single Color Image

  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
  • 2018
VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL

Detailed Full-Body Reconstructions of Moving People from Monocular RGB-D Sequences

  • 2015 IEEE International Conference on Computer Vision (ICCV)
  • 2015
VIEW 6 EXCERPTS
HIGHLY INFLUENTIAL

KinectFusion: Real-time dense surface mapping and tracking

  • 2011 10th IEEE International Symposium on Mixed and Augmented Reality
  • 2011
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Compositional Human Pose Regression

  • Computer Vision and Image Understanding
  • 2018
VIEW 1 EXCERPT