3. Method

We model the deformable object in a canonical time-
the rest body pose space, that can be
invariant space, i.e.
transformed to the articulated pose in the camera space at
each time instance with forward mappings, and transform
back with backward mappings. We use implicit functions
to represent the 3D shape, color, and dense semantic em-
beddings of the object. Our neural 3D model can be de-
formed and rendered into images at each time instance via
differentiable volume rendering, and optimized to ensure
consistency between the rendered images and multiple cues
in the observed images, including color, silhouette, optical
flow, and pixel feature embeddings. We refer readers to the
overview in Fig. 2 and the list of notations in supplement.

3.1. Shape, Appearance, and Warping Model

We first represent shape and appearance of deformable
BANMo- Building Animatable 3D Neural Models from Many Casual Videos