  Neural character models can now reconstruct detailed geometry and texture
from video, but they lack explicit shadows and shading, leading to artifacts
when generating novel views and poses or during relighting. It is particularly
difficult to include shadows as they are a global effect and the required
casting of secondary rays is costly. We propose a new shadow model using a
Gaussian density proxy that replaces sampling with a simple analytic formula.
It supports dynamic motion and is tailored for shadow computation, thereby
avoiding the affine projection approximation and sorting required by the
closely related Gaussian splatting. Combined with a deferred neural rendering
model, our Gaussian shadows enable Lambertian shading and shadow casting with
minimal overhead. We demonstrate improved reconstructions, with better
separation of albedo, shading, and shadows in challenging outdoor scenes with
direct sun light and hard shadows. Our method is able to optimize the light
direction without any input from the user. As a result, novel poses have fewer
shadow artifacts and relighting in novel scenes is more realistic compared to
the state-of-the-art methods, providing new ways to pose neural characters in
novel environments, increasing their applicability.
