  We introduce a new dataset called SyntheticFur built specifically for machine
learning training. The dataset consists of ray traced synthetic fur renders
with corresponding rasterized input buffers and simulation data files. We
procedurally generated approximately 140,000 images and 15 simulations with
Houdini. The images consist of fur groomed with different skin primitives and
move with various motions in a predefined set of lighting environments. We also
demonstrated how the dataset could be used with neural rendering to
significantly improve fur graphics using inexpensive input buffers by training
a conditional generative adversarial network with perceptual loss. We hope the
availability of such high fidelity fur renders will encourage new advances with
neural rendering for a variety of applications.
