tive comparison in Tab. 1.
Baseline setup. Nerfies [33] is designed for a single contin-
uously captured video, assuming object root body pose can
be compensated by background-SfM. In our setup, object
moves and background SfM does not provide root poses
for the object. When focused on the deformable object,
SfM (such as COLMAP) failed to converge due to violation
of rigidity, leading to very few successful registrations (18
over 900 images registered on casual-cat). To make a
fair comparison, we provide Nerfies with rough initial root
poses (obtained from our PoseNet, Sec. 3.4). After opti-
mization, meshes are extracted by running marching cubes
on a 2563 grid. Another baseline, ViSER [65], directly op-
timizes object shape and poses using optical flow, silhou-
ette, and color reconstruction losses.
It does not assume
category-level priors such as CSE features, and therefore
applicable to generic object categories. However, ViSER’s
BANMo- Building Animatable 3D Neural Models from Many Casual Videos