  Recent progress in neural rendering has brought forth pioneering methods,
such as NeRF and Gaussian Splatting, which revolutionize view rendering across
various domains like AR/VR, gaming, and content creation. While these methods
excel at interpolating {\em within the training data}, the challenge of
generalizing to new scenes and objects from very sparse views persists.
Specifically, modeling 3D humans from sparse views presents formidable hurdles
due to the inherent complexity of human geometry, resulting in inaccurate
reconstructions of geometry and textures. To tackle this challenge, this paper
leverages recent advancements in Gaussian Splatting and introduces a new method
to learn generalizable human Gaussians that allows photorealistic and accurate
view-rendering of a new human subject from a limited set of sparse views in a
feed-forward manner. A pivotal innovation of our approach involves
reformulating the learning of 3D Gaussian parameters into a regression process
defined on the 2D UV space of a human template, which allows leveraging the
strong geometry prior and the advantages of 2D convolutions. In addition, a
multi-scaffold is proposed to effectively represent the offset details. Our
method outperforms recent methods on both within-dataset generalization as well
as cross-dataset generalization settings.
