We construct the first markerless deformable interaction dataset recording interactive motions of the hands and deformable objects, called HMDO (Hand Manipulation with Deformable Objects). With our built multi-view capture system, it captures the deformable interactions with multiple perspectives, various object shapes, and diverse interactive forms. Our motivation is the current lack of hand and deformable object interaction datasets, as 3D hand and deformable object reconstruction is challenging. Mainly due to mutual occlusion, the interaction area is difficult to observe, the visual features between the hand and the object are entangled, and the reconstruction of the interaction area deformation is difficult. To tackle this challenge, we propose a method to annotate our captured data. Our key idea is to collaborate with estimated hand features to guide the object global pose estimation, and then optimize the deformation process of the object by analyzing the relationship between the hand and the object. Through comprehensive evaluation, the proposed method can reconstruct interactive motions of hands and deformable objects with high quality. HMDO currently consists of 21600 frames over 12 sequences. In the future, this dataset could boost the research of learning-based reconstruction of deformable interaction scenes.
翻译:我们建造了第一个无标记的变形互动数据集,记录手和变形物体的交互式动作,称为HMDO(用变形物体进行操纵)。我们建造了多视图捕捉系统,它捕捉了与多种视角、不同对象形状和不同互动形式的变形互动。我们的动机是目前缺乏手和变形物体互动数据集,因为3D手和变形物体的重建具有挑战性。主要由于相互隔离,互动区域难以观测,手和对象之间的视觉特征被缠绕,互动区域的重建也十分困难。为了应对这一挑战,我们提出了一个说明我们所捕取的数据的方法。我们的关键想法是,与估计的手特征合作,指导对象的全球表面估计,然后通过分析手与对象之间的关系优化对象的变形进程。通过全面评估,拟议的方法可以重建手和变形物体的交互动作和高品质的变形物体的移动。HMDO目前由21600框架组成,在12个序列上进行重组。在将来,可以学习的图像的重建中,这种数据变形研究可以优化。