  We present a novel animatable 3D Gaussian model for rendering high-fidelity
free-view human motions in real time. Compared to existing NeRF-based methods,
the model owns better capability in synthesizing high-frequency details without
the jittering problem across video frames. The core of our model is a novel
augmented 3D Gaussian representation, which attaches each Gaussian with a
learnable code. The learnable code serves as a pose-dependent appearance
embedding for refining the erroneous appearance caused by geometric
transformation of Gaussians, based on which an appearance refinement model is
learned to produce residual Gaussian properties to match the appearance in
target pose. To force the Gaussians to learn the foreground human only without
background interference, we further design a novel alpha loss to explicitly
constrain the Gaussians within the human body. We also propose to jointly
optimize the human joint parameters to improve the appearance accuracy. The
animatable 3D Gaussian model can be learned with shallow MLPs, so new human
motions can be synthesized in real time (66 fps on avarage). Experiments show
that our model has superior performance over NeRF-based methods.
