toggle visibility Search & Display Options

Select All    Deselect All
 |   | 
Details
   print
  Record Links
Author (down) Meysam Madadi; Hugo Bertiche; Sergio Escalera edit   pdf
doi  openurl
  Title Deep unsupervised 3D human body reconstruction from a sparse set of landmarks Type Journal Article
  Year 2021 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 129 Issue Pages 2499–2512  
  Keywords  
  Abstract In this paper we propose the first deep unsupervised approach in human body reconstruction to estimate body surface from a sparse set of landmarks, so called DeepMurf. We apply a denoising autoencoder to estimate missing landmarks. Then we apply an attention model to estimate body joints from landmarks. Finally, a cascading network is applied to regress parameters of a statistical generative model that reconstructs body. Our set of proposed loss functions allows us to train the network in an unsupervised way. Results on four public datasets show that our approach accurately reconstructs the human body from real world mocap data.  
  Address  
  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  
  Notes HUPBA; no proj;MILAB Approved no  
  Call Number Admin @ si @ MBE2021 Serial 3654  
Permanent link to this record
Select All    Deselect All
 |   | 
Details
   print

Save Citations:
Export Records: