  While recent advancements in animatable human rendering have achieved
remarkable results, they require test-time optimization for each subject which
can be a significant limitation for real-world applications. To address this,
we tackle the challenging task of learning a Generalizable Neural Human
Renderer (GNH), a novel method for rendering animatable humans from monocular
video without any test-time optimization. Our core method focuses on
transferring appearance information from the input video to the output image
plane by utilizing explicit body priors and multi-view geometry. To render the
subject in the intended pose, we utilize a straightforward CNN-based image
renderer, foregoing the more common ray-sampling or rasterizing-based rendering
modules. Our GNH achieves remarkable generalizable, photorealistic rendering
with unseen subjects with a three-stage process. We quantitatively and
qualitatively demonstrate that GNH significantly surpasses current
state-of-the-art methods, notably achieving a 31.3% improvement in LPIPS.
