often rely on a coarse shape template [18, 54, 70], and are
not able to recover fine-grained details or large deforma-
tions. Recently, HDNet [10] uses social media videos to
learn depth estimators for clothed human.
Category-agnostic video shape reconstruction. Non-
rigid structure from motion (NRSfM) methods [4, 8, 17,
19, 43] reconstruct non-rigid 3D shapes from a set of 2D
point trajectories in a class-agnostic way. However, due
to difficulties in obtaining accurate long-range correspon-
dences [40, 49], they do not work well for videos in the
wild. Recent efforts such as LASR and ViSER [64, 65] re-
construct articulated shapes from a monocular video with
differentiable rendering. As our results show, they may still
produce blurry geometry and unrealistic articulations.
Neural radiance fields. Prior works on NeRF optimize a
continuous scene function for novel view synthesis given
a set of images, often assuming the scene is rigid and
BANMo- Building Animatable 3D Neural Models from Many Casual Videos