  Human-object interactions with articulated objects are common in everyday
life. Despite much progress in single-view 3D reconstruction, it is still
challenging to infer an articulated 3D object model from an RGB video showing a
person manipulating the object. We canonicalize the task of articulated 3D
human-object interaction reconstruction from RGB video, and carry out a
systematic benchmark of five families of methods for this task: 3D plane
estimation, 3D cuboid estimation, CAD model fitting, implicit field fitting,
and free-form mesh fitting. Our experiments show that all methods struggle to
obtain high accuracy results even when provided ground truth information about
the observed objects. We identify key factors which make the task challenging
and suggest directions for future work on this challenging 3D computer vision
task. Short video summary at https://www.youtube.com/watch?v=5tAlKBojZwc
