  We propose Deep Residual Mixture Models (DRMMs), a novel deep generative
model architecture. Compared to other deep models, DRMMs allow more flexible
conditional sampling: The model can be trained once with all variables, and
then used for sampling with arbitrary combinations of conditioning variables,
Gaussian priors, and (in)equality constraints. This provides new opportunities
for interactive and exploratory machine learning, where one should minimize the
user waiting for retraining a model. We demonstrate DRMMs in constrained
multi-limb inverse kinematics and controllable generation of animations.
