  Solving image-to-3D from a single view is an ill-posed problem, and current
neural reconstruction methods addressing it through diffusion models still rely
on scene-specific optimization, constraining their generalization capability.
To overcome the limitations of existing approaches regarding generalization and
consistency, we introduce a novel neural rendering technique. Our approach
employs the signed distance function as the surface representation and
incorporates generalizable priors through geometry-encoding volumes and
HyperNetworks. Specifically, our method builds neural encoding volumes from
generated multi-view inputs. We adjust the weights of the SDF network
conditioned on an input image at test-time to allow model adaptation to novel
scenes in a feed-forward manner via HyperNetworks. To mitigate artifacts
derived from the synthesized views, we propose the use of a volume transformer
module to improve the aggregation of image features instead of processing each
viewpoint separately. Through our proposed method, dubbed as Hyper-VolTran, we
avoid the bottleneck of scene-specific optimization and maintain consistency
across the images generated from multiple viewpoints. Our experiments show the
advantages of our proposed approach with consistent results and rapid
generation.
