  Real-time rendering of photorealistic and controllable human avatars stands
as a cornerstone in Computer Vision and Graphics. While recent advances in
neural implicit rendering have unlocked unprecedented photorealism for digital
avatars, real-time performance has mostly been demonstrated for static scenes
only. To address this, we propose ASH, an animatable Gaussian splatting
approach for photorealistic rendering of dynamic humans in real-time. We
parameterize the clothed human as animatable 3D Gaussians, which can be
efficiently splatted into image space to generate the final rendering. However,
naively learning the Gaussian parameters in 3D space poses a severe challenge
in terms of compute. Instead, we attach the Gaussians onto a deformable
character model, and learn their parameters in 2D texture space, which allows
leveraging efficient 2D convolutional architectures that easily scale with the
required number of Gaussians. We benchmark ASH with competing methods on
pose-controllable avatars, demonstrating that our method outperforms existing
real-time methods by a large margin and shows comparable or even better results
than offline methods.
