  Accurate subsurface scattering solutions require the integration of optical
material properties along many complicated light paths. We present a method
that learns a simple geometric approximation of random paths in a homogeneous
volume of translucent material. The generated representation allows determining
the absorption along the path as well as a direct lighting contribution, which
is representative of all scattering events along the path. A sequence of
conditional variational auto-encoders (CVAEs) is trained to model the
statistical distribution of the photon paths inside a spherical region in
presence of multiple scattering events. A first CVAE learns to sample the
number of scattering events, occurring on a ray path inside the sphere, which
effectively determines the probability of the ray being absorbed. Conditioned
on this, a second model predicts the exit position and direction of the light
particle. Finally, a third model generates a representative sample of photon
position and direction along the path, which is used to approximate the
contribution of direct illumination due to in-scattering. To accelerate the
tracing of the light path through the volumetric medium toward the solid
boundary, we employ a sphere-tracing strategy that considers the light
absorption and is able to perform statistically accurate next-event estimation.
We demonstrate efficient learning using shallow networks of only three layers
and no more than 16 nodes. In combination with a GPU shader that evaluates the
CVAEs' predictions, performance gains can be demonstrated for a variety of
different scenarios. A quality evaluation analyzes the approximation error that
is introduced by the data-driven scattering simulation and sheds light on the
major sources of error in the accelerated path tracing process.

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