  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 does
not represent hand shape accurately 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 50+ 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.
