Detailed, accurate, human shape estimation from clothed 3D scan sequences
This work addresses the practical barrier of requiring minimal clothing for 3D body scanning, benefiting applications in virtual reality and health monitoring, though it is incremental as it builds on existing body models.
The paper tackles the problem of estimating human pose and body shape from 3D scans of clothed individuals, enabling applications like virtual try-on and health monitoring, and demonstrates that their method outperforms state-of-the-art approaches in both pose and shape estimation with quantitative improvements.
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.