Existing marker-less motion capture methods often assume known backgrounds,
static cameras, and sequence specific motion priors, which narrows its
application scenarios. Here we propose a fully automatic method that given
multi-view video, estimates 3D human motion and body shape. We take recent
SMPLify \cite{bogo2016keep} as the base method, and extend it in several ways.
First we fit the body to 2D features detected in multi-view images. Second, we
use a CNN method to segment the person in each image and fit the 3D body model
to the contours to further improves accuracy. Third we utilize a generic and
robust DCT temporal prior to handle the left and right side swapping issue
sometimes introduced by the 2D pose estimator. Validation on standard
benchmarks shows our results are comparable to the state of the art and also
provide a realistic 3D shape avatar. We also demonstrate accurate results on
HumanEva and on challenging dance sequences from YouTube in monocular case.

本文提出了一种基于 SMPLify 方法的全自动方法，可以从多视角视频中估计三维人体运动和身体形状，并利用 CNN 方法和 DCT 时间先验方法来提高精度，并且在标准基准测试中的结果与最先进的方法相当，同时还提供了一个逼真的 3D 形状 Avatar。