  We consider the problem of predicting the 3D shape, articulation, viewpoint,
texture, and lighting of an articulated animal like a horse given a single test
image as input. We present a new method, dubbed MagicPony, that learns this
predictor purely from in-the-wild single-view images of the object category,
with minimal assumptions about the topology of deformation. At its core is an
implicit-explicit representation of articulated shape and appearance, combining
the strengths of neural fields and meshes. In order to help the model
understand an object's shape and pose, we distil the knowledge captured by an
off-the-shelf self-supervised vision transformer and fuse it into the 3D model.
To overcome local optima in viewpoint estimation, we further introduce a new
viewpoint sampling scheme that comes at no additional training cost. MagicPony
outperforms prior work on this challenging task and demonstrates excellent
generalisation in reconstructing art, despite the fact that it is only trained
on real images.
