: Given static 3D scans or 3D scan sequences (in pink), we estimate the naked shape under clothing (beige). Our method obtains accurate results by minimizing an objective function that captures the visible details of the skin, while being robust to clothing. We show several pairs of clothed scan sequences and the estimated body shape underneath. We address the problem of estimating human pose and body shape from 3D scans over time. Reliable estimation of 3D body shape is necessary for many applications including virtual try-on, health monitoring, and avatar creation for virtual reality. Scanning bodies in minimal clothing, however, presents a practical barrier to these applications. We address this problem by estimating body shape under clothing from a sequence of 3D scans. Previous methods that have exploited body models produce smooth shapes lacking personalized details. We contribute a new approach to recover a personalized shape of the person. The estimated shape deviates from a parametric model to fit the 3D scans. We demonstrate the method using high quality 4D data as well as sequences of visual hulls extracted from multi-view images. We also make available BUFF, a new 4D dataset that enables quantitative evaluation http://buff.is.tue.mpg.de/. Our method outperforms the state of the art in both pose estimation and shape estimation, qualitatively and quantitatively.
BUFF REF supplies high-quality 4D scans of clothed bodies, but the body shapes and the garment categories are quite limited.
Detailed, Accurate, Human Shape Estimation from Clothed 3D Scan Sequences
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science