Animatable 3D Gaussians for High-fidelity Synthesis of Human Motions

Understand

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.