Detailed, accurate, human shape estimation from clothed 3D scan sequences

TL;DR

This paper presents a novel method for estimating personalized human body shapes from clothed 3D scan sequences, improving accuracy over previous models and providing a new dataset for evaluation.

Contribution

A new approach that recovers detailed, personalized human shapes from clothed 3D scans, deviating from parametric models for better fit and detail.

Findings

Outperforms state-of-the-art in pose and shape estimation

Demonstrates effectiveness on high-quality 4D data and visual hull sequences

Provides BUFF, a new dataset for quantitative evaluation

Abstract

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.