  Implicit neural representations, represented by Neural Radiance Fields
(NeRF), have dominated research in 3D computer vision by virtue of high-quality
visual results and data-driven benefits. However, their realistic applications
are hindered by the need for dense inputs and per-scene optimization. To solve
this problem, previous methods implement generalizable NeRFs by extracting
local features from sparse inputs as conditions for the NeRF decoder. However,
although this way can allow feed-forward reconstruction, they suffer from the
inherent drawback of yielding sub-optimal results caused by erroneous
reprojected features. In this paper, we focus on this problem and aim to
address it by introducing pre-trained generative priors to enable high-quality
generalizable novel view synthesis. Specifically, we propose a novel Indirect
Diffusion-guided NeRF framework, termed ID-NeRF, which leverages pre-trained
diffusion priors as a guide for the reprojected features created by the
previous paradigm. Notably, to enable 3D-consistent predictions, the proposed
ID-NeRF discards the way of direct supervision commonly used in prior 3D
generative models and instead adopts a novel indirect prior injection strategy.
This strategy is implemented by distilling pre-trained knowledge into an
imaginative latent space via score-based distillation, and an attention-based
refinement module is then proposed to leverage the embedded priors to improve
reprojected features extracted from sparse inputs. We conduct extensive
experiments on multiple datasets to evaluate our method, and the results
demonstrate the effectiveness of our method in synthesizing novel views in a
generalizable manner, especially in sparse settings.
