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Author (up) Hunor Laczko; Meysam Madadi; Sergio Escalera; Jordi Gonzalez edit   pdf
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  Title A Generative Multi-Resolution Pyramid and Normal-Conditioning 3D Cloth Draping Type Conference Article
  Year 2024 Publication Winter Conference on Applications of Computer Vision Abbreviated Journal  
  Volume Issue Pages 8709-8718  
  Keywords  
  Abstract RGB cloth generation has been deeply studied in the related literature, however, 3D garment generation remains an open problem. In this paper, we build a conditional variational autoencoder for 3D garment generation and draping. We propose a pyramid network to add garment details progressively in a canonical space, i.e. unposing and unshaping the garments w.r.t. the body. We study conditioning the network on surface normal UV maps, as an intermediate representation, which is an easier problem to optimize than 3D coordinates. Our results on two public datasets, CLOTH3D and CAPE, show that our model is robust, controllable in terms of detail generation by the use of multi-resolution pyramids, and achieves state-of-the-art results that can highly generalize to unseen garments, poses, and shapes even when training with small amounts of data.  
  Address Waikoloa; Hawai; USA; January 2024  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference WACV  
  Notes ISE; HUPBA;MILAB Approved no  
  Call Number Admin @ si @ LME2024 Serial 3996  
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