Figure 2. Method overview. BANMo optimizes a set of shape and deformation parameters (Sec. 3.1) that describe the video observations
in pixel colors, silhouettes, optical flow, and higher-order features descriptors, based on a differentiable volume rendering framework.
BANMo uses a neural blend skinning model (Sec. 3.2) to transform 3D points between the camera space and the canonical space, enabling
handling large deformations. To register pixels across videos, BANMo jointly optimizes an implicit canonical embedding (CE) (Sec. 3.3).
a canonical embedding ψ ∈ R16. These properties are pre-
dicted by the Multilayer Perceptron (MLP) networks:

ct = MLPc(X∗, vt, ωt
e),
σ = Γβ(MLPSDF(X∗)),
ψ = MLPψ(X∗).

(1)
(2)
(3)

As in NeRF, color ct also depends on a time-varying view
e∈R64
direction vt∈R2 and a learnable environment code ωt
that captures environment illumination conditions [27].
BANMo- Building Animatable 3D Neural Models from Many Casual Videos