论文标题
性别，姿势和摄像头距离对人体维度估计的影响
Effect of Gender, Pose and Camera Distance on Human Body Dimensions Estimation
论文作者
论文摘要
人体尺寸估计（HBDE）是智能代理可以执行的任务，以尝试从图像（2D）或点云或网格（3D）确定人体信息。更具体地说，如果我们将HBDE问题定义为从图像中推断人体测量值，那么HBDE是一个困难，逆，多任务回归问题，可以通过机器学习技术（尤其是卷积神经网络（CNN））来解决。尽管社区为推进人类形态分析做出了巨大的努力，但缺乏系统的实验来评估CNNS对图像的人体维度的估计。我们的贡献在于在一系列受控实验中评估CNN估计性能。为此，我们通过使用不同的相机距离渲染图像来增强最近发布的神经拟人计数据集。我们评估了估计和实际HBD之间的网络推断绝对和相对平均误差。我们在四种情况下训练和评估CNN：（1）对特定性别的受试者的训练，（2）在特定的姿势中，（3）稀疏的摄像头距离和（4）密集的相机距离。我们的实验不仅表明网络可以成功执行任务，而且还揭示了许多相关事实，这些事实有助于更好地了解HBDE的任务。
Human Body Dimensions Estimation (HBDE) is a task that an intelligent agent can perform to attempt to determine human body information from images (2D) or point clouds or meshes (3D). More specifically, if we define the HBDE problem as inferring human body measurements from images, then HBDE is a difficult, inverse, multi-task regression problem that can be tackled with machine learning techniques, particularly convolutional neural networks (CNN). Despite the community's tremendous effort to advance human shape analysis, there is a lack of systematic experiments to assess CNNs estimation of human body dimensions from images. Our contribution lies in assessing a CNN estimation performance in a series of controlled experiments. To that end, we augment our recently published neural anthropometer dataset by rendering images with different camera distance. We evaluate the network inference absolute and relative mean error between the estimated and actual HBDs. We train and evaluate the CNN in four scenarios: (1) training with subjects of a specific gender, (2) in a specific pose, (3) sparse camera distance and (4) dense camera distance. Not only our experiments demonstrate that the network can perform the task successfully, but also reveal a number of relevant facts that contribute to better understand the task of HBDE.
