Articulated Pose Estimation in a Learned Smooth Space of Feasible Solutions
Articulated Pose Estimation in a Learned Smooth Space of Feasible Solutions
复制标题
可行解决方案的已知平滑空间中的铰接姿势估计
DOI：
10.1109/cvpr.2005.414
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发表时间：
2005
期刊：
影响因子：
--
通讯作者：
S. Sclaroff
中科院分区：
文献类型：
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作者：
Tai;Rui Li;S. Sclaroff
提出了一种基于学习的框架，用于从单个图像估计人体姿势。给定一个从姿势空间映射到图像特征空间的可微函数，目标是反转该过程：仅在给定图像特征的情况下估计姿势。逆映射是一个不适定问题，因为逆映射是一对多过程，因此存在多种解决方案。希望将解空间限制为可行解的较小子集。可行解的空间可能不允许封闭形式的描述。所提出的框架旨在学习这样一个空间的近似值。使用高斯过程潜变量建模。缩放共轭梯度方法用于在学习空间中找到最佳匹配姿势。该公式允许轻松合并各种约束，以实现更准确的姿态估计。该方法的性能在从轮廓估计上身姿势的任务中进行了评估，并与专用映射架构进行了比较。所提出的方法在合成数据的估计准确性方面比后一种方法表现更好，并且在人类执行手势的真实视频中得到更好的定性结果。
A learning based framework is proposed for estimating human body pose from a single image. Given a differentiable function that maps from pose space to image feature space, the goal is to invert the process: estimate the pose given only image features. The inversion is an ill-posed problem as the inverse mapping is a one to many process, hence multiple solutions exist. It is desirable to restrict the solution space to a smaller subset of feasible solutions. The space of feasible solutions may not admit a closed form description. The proposed framework seeks to learn an approximation over such a space. Using Gaussian Process Latent Variable Modelling. The scaled conjugate gradient method is used to find the best matching pose in the learned space. The formulation allows easy incorporation of various constraints for more accurate pose estimation. The performance of the proposed approach is evaluated in the task of upper-body pose estimation from silhouettes and compared with the Specialized Mapping Architecture. The proposed approach performs better than the latter approach in terms of estimation accuracy with synthetic data and qualitatively better results with real video of humans performing gestures.