3D Congealing: 3D-Aware Image Alignment in the Wild
<|reference_start|>3D Congealing: 3D-Aware Image Alignment in the Wild: 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.<|reference_end|>
@article{zhang20243d,
    title={3D Congealing: 3D-Aware Image Alignment in the Wild},
    author={Yunzhi Zhang, Zizhang Li, Amit Raj, Andreas Engelhardt, Yuanzhen Li,
  Tingbo Hou, Jiajun Wu, Varun Jampani},
    journal={arXiv preprint arXiv:2404.02125},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.02125},
    primaryClass={cs.CV}
}