We are thus interested in reconstructing 3D deformable
objects from casually collected videos. However, individ-
ual videos may not contain sufficient information to obtain
good reconstruction of a given subject. Fortunately, we can
expect that users may collect several videos of the same sub-
jects, such as filming a family member or a pet over the span
of several months or years. In this case, we wish our system
to gather information from all available videos into a single
3D model, bridging any time discontinuity.

In this paper, we present BANMo, a Builder of
Animatable 3D Neural Models from multiple casual RGB
videos. By consolidating the 2D cues from thousands of
images into a fixed canonical space, BANMo learns a high-
BANMo- Building Animatable 3D Neural Models from Many Casual Videos