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Project page for 'Neural Congealing: Aligning Images to a Joint Semantic Atlas.'
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Neural Congealing: Aligning Images to a Joint Semantic Atlas Neural Congealing: Aligning Images to a Joint Semantic Atlas CVPR 2023 Dolev Ofri-Amar 1 Michal Geyer 1 Yoni Kasten 2 Tali Dekel 1 1 Weizmann Institute of Science 2 NVIDIA Research | Paper | Supplementary Material | Code | Abstract We present Neural Congealing -- a zero-shot self-supervised framework for detecting and jointly aligning semantically-common content across a given set of images. Our approach harnesses the power of pre-trained DINO-ViT features to learn: (i) a joint semantic atlas -- a 2D grid that captures the mode of DINO-ViT features in the input set, and (ii) dense mappings from the unified atlas to each of the input images. We derive a new robust self-supervised framework that optimizes the atlas representation and mappings per image set, requiring only a few real-world images as input without any additional input information (e.g., segmentation masks). Nota...
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