  Neural Radiance Fields (NeRF) have become an increasingly popular
representation to capture high-quality appearance and shape of scenes and
objects. However, learning generalizable NeRF priors over categories of scenes
or objects has been challenging due to the high dimensionality of network
weight space. To address the limitations of existing work on generalization,
multi-view consistency and to improve quality, we propose HyP-NeRF, a latent
conditioning method for learning generalizable category-level NeRF priors using
hypernetworks. Rather than using hypernetworks to estimate only the weights of
a NeRF, we estimate both the weights and the multi-resolution hash encodings
resulting in significant quality gains. To improve quality even further, we
incorporate a denoise and finetune strategy that denoises images rendered from
NeRFs estimated by the hypernetwork and finetunes it while retaining multiview
consistency. These improvements enable us to use HyP-NeRF as a generalizable
prior for multiple downstream tasks including NeRF reconstruction from
single-view or cluttered scenes and text-to-NeRF. We provide qualitative
comparisons and evaluate HyP-NeRF on three tasks: generalization, compression,
and retrieval, demonstrating our state-of-the-art results.
