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  Tex2Shape: Detailed Full Human Body Geometry From a Single Image

Alldieck, T., Pons-Moll, G., Theobalt, C., & Magnor, M. A. (2019). Tex2Shape: Detailed Full Human Body Geometry From a Single Image. Retrieved from http://arxiv.org/abs/1904.08645.

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arXiv:1904.08645.pdf (Preprint), 6MB
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arXiv:1904.08645.pdf
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 Urheber:
Alldieck, Thiemo1, Autor           
Pons-Moll, Gerard1, Autor           
Theobalt, Christian2, Autor           
Magnor, Marcus A.3, Autor           
Affiliations:
1Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society, ou_persistent22              
2Computer Graphics, MPI for Informatics, Max Planck Society, ou_40047              
3External Organizations, ou_persistent22              

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Schlagwörter: Computer Science, Computer Vision and Pattern Recognition, cs.CV
 Zusammenfassung: We present a simple yet effective method to infer detailed full human body
shape from only a single photograph. Our model can infer full-body shape
including face, hair, and clothing including wrinkles at interactive
frame-rates. Results feature details even on parts that are occluded in the
input image. Our main idea is to turn shape regression into an aligned
image-to-image translation problem. The input to our method is a partial
texture map of the visible region obtained from off-the-shelf methods. From a
partial texture, we estimate detailed normal and vector displacement maps,
which can be applied to a low-resolution smooth body model to add detail and
clothing. Despite being trained purely with synthetic data, our model
generalizes well to real-world photographs. Numerous results demonstrate the
versatility and robustness of our method.

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Sprache(n): eng - English
 Datum: 2019-04-182019-09-152019
 Publikationsstatus: Online veröffentlicht
 Seiten: 13 p.
 Ort, Verlag, Ausgabe: -
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 Identifikatoren: arXiv: 1904.08645
URI: http://arxiv.org/abs/1904.08645
BibTex Citekey: Alldieck_arXiv1904.08645
 Art des Abschluß: -

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