  We present Neural Articulated Radiance Field (NARF), a novel deformable 3D
representation for articulated objects learned from images. While recent
advances in 3D implicit representation have made it possible to learn models of
complex objects, learning pose-controllable representations of articulated
objects remains a challenge, as current methods require 3D shape supervision
and are unable to render appearance. In formulating an implicit representation
of 3D articulated objects, our method considers only the rigid transformation
of the most relevant object part in solving for the radiance field at each 3D
location. In this way, the proposed method represents pose-dependent changes
without significantly increasing the computational complexity. NARF is fully
differentiable and can be trained from images with pose annotations. Moreover,
through the use of an autoencoder, it can learn appearance variations over
multiple instances of an object class. Experiments show that the proposed
method is efficient and can generalize well to novel poses. We make the code,
model and demo available for research purposes at
https://github.com/nogu-atsu/NARF
