一种基于三维骨架化的人体姿势估计方法

提出了一种基于三维2 步子迭代混合细化算法和启发式搜索的无标记三维人体姿势估计方法.其主要思想是首先通过一种新的2 步子迭代混合细化算法,对由Shape-from-Silhouette(SFS)算法从多幅二维图像中重建出的人体三维模型进行骨架化,然后通过一种启发式的方法进行模型匹配.实验结果表明,该方法不仅思想简单,而且具有较好的鲁棒性,能够很好的去除各种噪声的影响.

A 3D 2-Subiteration Thinning Algorithm for Human Pose Estimation

Markerless human body model reconstruction has been a hot research topic for decades, and it has many applications in human computer interaction (HCI), medical rehabilitation, sport training, and virtual human synthesis for games, cartoons and digital films. This paper presents a markerless 3D human body skeleton model reconstruction method. It takes input from multi-view video, and computes the visual hull of human body through shape-from-silhouette. It then extracts the 3D skeleton by a new hybrid 2-subiteration thinning algorithm, and registers the skeleton to a 3D human body skeleton model by a heuristic method. The experiment results show that although the intermediate results of the shape-from-silhouettes process and 3D thinning process contain much noise, the model registration method, which bases on a set of simple prior rules , can still get accurate 3D human body skeleton model.