ClothCap: Seamless 4D Clothing Capture and Retargeting




Designing and simulating realistic clothing is challenging and, while several methods have addressed the capture of clothing from 3D scans, previous methods have been limited to single garments and simple motions, lack detail, or require specialized texture pat- terns. Here we address the problem of capturing regular clothing on fully dressed people in motion. People typically wear multiple pieces of clothing at a time. To estimate the shape of such clothing, track it over time, and render it believably, each garment must be segmented from the others and the body. Our ClothCap approach uses a new multi-part 3D model of clothed bodies, automatically segments each piece of clothing, estimates the naked body shape and pose under the clothing, and tracks the 3D deformations of the clothing over time. We estimate the garments and their mo- tion from 4D scans; that is, high-resolution 3D scans of the subject in motion at 60 fps. The model allows us to capture a clothed per- son in motion, extract their clothing, and retarget the clothing to new body shapes. ClothCap provides a step towards virtual try-on with a technology for capturing, modeling, and analyzing clothing in motion.

Author(s): Gerard Pons-Moll and Sergi Pujades and Sonny Hu and Michael Black
Journal: ACM Transactions on Graphics, (Proc. SIGGRAPH) [conditionally accepted]
Year: 2017

Department(s): Perceiving Systems
Bibtex Type: Article (article)
Paper Type: Journal

Note: Two first authors contributed equally


  title = {ClothCap: Seamless 4D Clothing Capture and Retargeting},
  author = {Pons-Moll, Gerard and Pujades, Sergi and Hu, Sonny and Black, Michael},
  journal = {ACM Transactions on Graphics, (Proc. SIGGRAPH) [conditionally accepted]},
  year = {2017},
  note = {},
  crossref = {}