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2022

3D Textured Shape Recovery with Learned Geometric Priors

Li, Lei, Liu, Zhizheng, Ren, Weining et al.

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3D textured shape recovery from partial scans is crucial for many real-world applications.

Existing approaches have demonstrated the efficacy of implicit function representation, but they suffer from partial inputs with severe occlusions and varying object types, which greatly hinders their application value in the real world.
This technical report presents our approach to address these limitations by incorporating learned geometric priors.
To this end, we generate a SMPL model from learned pose prediction and fuse it into the partial input to add prior knowledge of human bodies.

3D Textured Shape Recovery with Learned Geometric Priors · Around

Built on

Adam: A method for stochastic optimization
Original
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Smpl: A skinned multi-person linear model
M. Loper, N. Mahmood, J. Romero, G. Pons-Moll, and M. J. Black · 2015
Earlier work this paper cites.
Instance normalization: The missing ingredient for fast stylization
Original
D. Ulyanov, A. Vedaldi, and V. Lempitsky · 2016
Earlier work this paper cites.

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