论文标题
Humman：用于多功能传感和建模的多模式4D人类数据集
HuMMan: Multi-Modal 4D Human Dataset for Versatile Sensing and Modeling
论文作者
论文摘要
4D人类的传感和建模是具有许多应用的视觉和图形中的基本任务。随着新传感器和算法的进步，对更多通用数据集的需求不断增加。在这项工作中，我们贡献了Humman，这是一个大规模的多模式4D人类数据集，具有1000个人类受试者，400K序列和60m帧。 Humman具有多种吸引人的属性：1）多模式数据和注释，包括颜色图像，点云，关键点，SMPL参数和纹理网格； 2）传感器套件中包括流行的移动设备； 3）一组500个动作，旨在涵盖基本运动； 4）支持和评估多个任务，例如动作识别，姿势估计，参数人的恢复和纹理网格重建。关于Humman的广泛实验，需要进一步研究诸如细粒度识别，动态人类网格重建，基于点云的参数人恢复和跨设备域间隙等挑战。
4D human sensing and modeling are fundamental tasks in vision and graphics with numerous applications. With the advances of new sensors and algorithms, there is an increasing demand for more versatile datasets. In this work, we contribute HuMMan, a large-scale multi-modal 4D human dataset with 1000 human subjects, 400k sequences and 60M frames. HuMMan has several appealing properties: 1) multi-modal data and annotations including color images, point clouds, keypoints, SMPL parameters, and textured meshes; 2) popular mobile device is included in the sensor suite; 3) a set of 500 actions, designed to cover fundamental movements; 4) multiple tasks such as action recognition, pose estimation, parametric human recovery, and textured mesh reconstruction are supported and evaluated. Extensive experiments on HuMMan voice the need for further study on challenges such as fine-grained action recognition, dynamic human mesh reconstruction, point cloud-based parametric human recovery, and cross-device domain gaps.
