We address in-the-wild hand-object reconstruction for a known object category in egocentric videos, focusing on temporal periods of stable grasps. We propose the task of Hand-Object Stable Grasp Reconstruction (HO-SGR), the joint reconstruction of frames during which the hand is stably holding the object. We thus can constrain the object motion relative to the hand, effectively regularising the reconstruction and improving performance. By analysing the 3D ARCTIC dataset, we identify temporal periods where the contact area between the hand and object vertices remain stable. We showcase that objects within stable grasps move within a single degree of freedom (1~DoF). We thus propose a method for jointly optimising all frames within a stable grasp by minimising the object's rotation to that within a latent 1 DoF. We then extend this knowledge to in-the-wild egocentric videos by labelling 2.4K clips of stable grasps from the EPIC-KITCHENS dataset. Our proposed EPIC-Grasps dataset includes 390 object instances of 9 categories, featuring stable grasps from videos of daily interactions in 141 environments. Our method achieves significantly better HO-SGR, both qualitatively and by computing the stable grasp area and 2D projection labels of mask overlaps.

我们提出了一种在日常环境下进行手-物体重建的方法，通过对稳定握持期间的帧进行联合重建，约束物体运动并提高重建性能。我们通过分析3D ARCTIC数据集，确定了手和物体之间接触区域保持稳定的时间段，并用实验展示了稳定握持期间物体仅在一个自由度内运动。然后，我们将这一方法扩展到真实环境中的egocentric视频，通过标记EPIC-KITCHENS数据集中稳定握持的剪辑，建立了EPIC-Grasps数据集，其中包括了141个环境中的390个物体实例，分为9个类别，并通过计算稳定握持区域和2D投影标签的掩码重叠，定量地展示了我们方法在稳定握持重建方面的显著改进。

握紧：在自视角视频中重建手-物体稳定握持