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Self-Supervised Learning of Semantically Consistent Visual Features via a Simple 3D Prior
Visual features extracted from vision models like DINOv2 or Stable Diffusion enable high quality semantic correspondence between images. However, such features exhibit documented limitations that hinder accurate correspondence estimation such as the inability to disambiguate the left from right for
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