  We propose an unsupervised method for 3D geometry-aware representation
learning of articulated objects, in which no image-pose pairs or foreground
masks are used for training. Though photorealistic images of articulated
objects can be rendered with explicit pose control through existing 3D neural
representations, these methods require ground truth 3D pose and foreground
masks for training, which are expensive to obtain. We obviate this need by
learning the representations with GAN training. The generator is trained to
produce realistic images of articulated objects from random poses and latent
vectors by adversarial training. To avoid a high computational cost for GAN
training, we propose an efficient neural representation for articulated objects
based on tri-planes and then present a GAN-based framework for its unsupervised
training. Experiments demonstrate the efficiency of our method and show that
GAN-based training enables the learning of controllable 3D representations
without paired supervision.

Computer Vision and Pattern Recognition