Anthropometric clothing measurements from 3D body scans
This work addresses the need for accurate and automated anthropometric measurement extraction for applications in clothing and fashion, representing an incremental improvement with specific domain focus.
The paper tackles the problem of acquiring anthropometric clothing measurements from 3D body scans by proposing a full processing pipeline, achieving mean absolute errors ranging from 2.5 mm to 16.0 mm across different measurements.
We propose a full processing pipeline to acquire anthropometric measurements from 3D measurements. The first stage of our pipeline is a commercial point cloud scanner. In the second stage, a pre-defined body model is fitted to the captured point cloud. We have generated one male and one female model from the SMPL library. The fitting process is based on non-rigid Iterative Closest Point (ICP) algorithm that minimizes overall energy of point distance and local stiffness energy terms. In the third stage, we measure multiple circumference paths on the fitted model surface and use a non-linear regressor to provide the final estimates of anthropometric measurements. We scanned 194 male and 181 female subjects and the proposed pipeline provides mean absolute errors from 2.5 mm to 16.0 mm depending on the anthropometric measurement.