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Tex2Shape: Detailed Full Human Body Geometry From a Single Image

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arxiv 1904.08645 v2 pith:RJQT7FEX submitted 2019-04-18 cs.CV

classification cs.CV
keywords bodydetailedmethodmodelshapeclothingfullhuman
verification ladder T0 review T1 audit T2 compute T3 formal

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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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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. 360-Degree Textures of People in Clothing from a Single Image

    cs.CV 2019-08 conditional novelty 6.0 of 10

    A single image is enough to predict a person's full 360-degree texture, clothing segmentation, and geometry in the SMPL UV-space, yielding a controllable 3D avatar.

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