In this episode, we discuss SHIC: Shape-Image Correspondences with no Keypoint Supervision by Aleksandar Shtedritski, Christian Rupprecht, Andrea Vedaldi. The paper introduces SHIC, a novel method for learning canonical surface mappings without manual supervision by using foundation models such as DINO and Stable Diffusion. SHIC simplifies the task to image-to-image correspondence prediction, outperforming some supervised techniques. The method uses non-photorealistic template renders to effectively simulate manual annotation, allowing reliable canonical map creation for diverse objects.
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