Finally, the MLPψ network maps 3D points to a canoni-
cal feature embedding ψ that can be matched by pixels from
different viewpoints and lighting conditions, enabling long-
range correspondence across videos. This feature can be
interpreted as a variant of Continuous Surface Embeddings
(CSE) [30] but defined volumetrically.
Space-time warping model. We consider a pair of time-
forward warping function
dependent warping functions:
W t,→ : X∗ → Xt mapping canonical location X∗ to
camera space location Xt at current time and the backward
warping function W t,← : Xt → X∗ for inverse mapping.
Prior work such as Nerfies [33] and Neural Scene Flow
Fields (NSFF) [22] learn deformations under the assump-
tions of known camera poses and small object deforma-
tion. As detailed in Sec. 3.2 and Sec. 3.4, we do not
BANMo- Building Animatable 3D Neural Models from Many Casual Videos