  We propose 3D Congealing, a novel problem of 3D-aware alignment for 2D images
capturing semantically similar objects. Given a collection of unlabeled
Internet images, our goal is to associate the shared semantic parts from the
inputs and aggregate the knowledge from 2D images to a shared 3D canonical
space. We introduce a general framework that tackles the task without assuming
shape templates, poses, or any camera parameters. At its core is a canonical 3D
representation that encapsulates geometric and semantic information. The
framework optimizes for the canonical representation together with the pose for
each input image, and a per-image coordinate map that warps 2D pixel
coordinates to the 3D canonical frame to account for the shape matching. The
optimization procedure fuses prior knowledge from a pre-trained image
generative model and semantic information from input images. The former
provides strong knowledge guidance for this under-constraint task, while the
latter provides the necessary information to mitigate the training data bias
from the pre-trained model. Our framework can be used for various tasks such as
correspondence matching, pose estimation, and image editing, achieving strong
results on real-world image datasets under challenging illumination conditions
and on in-the-wild online image collections.
