Building statistical shape spaces for 3D human modeling
作者：
Highlights：
• Expressive 3D human shape models are proposed.
• The models are learned from the largest available dataset of laser scans.
• Various template fitting and posture normalization approaches are evaluated.
• High quality of the learned shape spaces is empirically demonstrated.
• Proposed models and code to data pre-processing and model fitting are released.
摘要
•Expressive 3D human shape models are proposed.•The models are learned from the largest available dataset of laser scans.•Various template fitting and posture normalization approaches are evaluated.•High quality of the learned shape spaces is empirically demonstrated.•Proposed models and code to data pre-processing and model fitting are released.
论文关键词：Statistical human body model,Non-rigid template fitting
论文评审过程：Received 20 April 2016, Revised 12 January 2017, Accepted 12 February 2017, Available online 20 February 2017, Version of Record 24 February 2017.
论文官网地址：https://doi.org/10.1016/j.patcog.2017.02.018
