Understanding how we grasp objects with our hands has important applications
in areas like robotics and mixed reality. However, this challenging problem
requires accurate modeling of the contact between hands and objects. To capture
grasps, existing methods use skeletons, meshes, or parametric models that can
cause misalignments resulting in inaccurate contacts. We present MANUS, a
method for Markerless Hand-Object Grasp Capture using Articulated 3D Gaussians.
We build a novel articulated 3D Gaussians representation that extends 3D
Gaussian splatting for high-fidelity representation of articulating hands.
Since our representation uses Gaussian primitives, it enables us to efficiently
and accurately estimate contacts between the hand and the object. For the most
accurate results, our method requires tens of camera views that current
datasets do not provide. We therefore build MANUS-Grasps, a new dataset that
contains hand-object grasps viewed from 53 cameras across 30+ scenes, 3
subjects, and comprising over 7M frames. In addition to extensive qualitative
results, we also show that our method outperforms others on a quantitative
contact evaluation method that uses paint transfer from the object to the hand.

使用关节化的 3D 高斯方法捕获手 - 物体抓取，建立 MANUS-Grasps 数据集并展示比其他方法更准确的联系评估方法。

MANUS：利用弯曲的 3D 高斯模型捕获无标记手 - 物体握取

MANUS: Markerless Hand-Object Grasp Capture using Articulated 3D  Gaussians

Humans excel at grasping objects and manipulating them. Capturing human
grasps is important for understanding grasping behavior and reconstructing it
realistically in Virtual Reality (VR). However, grasp capture - capturing the
pose of a hand grasping an object, and orienting it w.r.t. the object - is
difficult because of the complexity and diversity of the human hand, and
occlusion. Reflective markers and magnetic trackers traditionally used to
mitigate this difficulty introduce undesirable artifacts in images and can
interfere with natural grasping behavior. We present preliminary work on a
completely marker-less algorithm for grasp capture from a video depicting a
grasp. We show how recent advances in 2D hand pose estimation can be used with
well-established optimization techniques. Uniquely, our algorithm can also
capture hand-object contact in detail and integrate it in the grasp capture
process. This is work in progress, find more details at this https URL
cc.gatech.edu/grasp_capture.html.

研究了通过使用最新的二维手姿势估计技术和优化技术进行的无标记算法来捕捉手抓握物体的过程，此算法也可以详细地捕捉手 - 物体接触并将其集成进抓握捕捉过程中。