  Neural radiance fields are capable of reconstructing high-quality drivable
human avatars but are expensive to train and render and not suitable for
multi-human scenes with complex shadows. To reduce consumption, we propose
Animatable 3D Gaussian, which learns human avatars from input images and poses.
We extend 3D Gaussians to dynamic human scenes by modeling a set of skinned 3D
Gaussians and a corresponding skeleton in canonical space and deforming 3D
Gaussians to posed space according to the input poses. We introduce a
multi-head hash encoder for pose-dependent shape and appearance and a
time-dependent ambient occlusion module to achieve high-quality reconstructions
in scenes containing complex motions and dynamic shadows. On both novel view
synthesis and novel pose synthesis tasks, our method achieves higher
reconstruction quality than InstantAvatar with less training time (1/60), less
GPU memory (1/4), and faster rendering speed (7x). Our method can be easily
extended to multi-human scenes and achieve comparable novel view synthesis
results on a scene with ten people in only 25 seconds of training.
