  Hand image synthesis and pose estimation from RGB images are both highly
challenging tasks due to the large discrepancy between factors of variation
ranging from image background content to camera viewpoint. To better analyze
these factors of variation, we propose the use of disentangled representations
and a disentangled variational autoencoder (dVAE) that allows for specific
sampling and inference of these factors. The derived objective from the
variational lower bound as well as the proposed training strategy are highly
flexible, allowing us to handle cross-modal encoders and decoders as well as
semi-supervised learning scenarios. Experiments show that our dVAE can
synthesize highly realistic images of the hand specifiable by both pose and
image background content and also estimate 3D hand poses from RGB images with
accuracy competitive with state-of-the-art on two public benchmarks.
