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PriorPose is proposed, a reference-guided correspondence framework that keeps the category prior explicit and solves canonicalization and alignment jointly in a shared feature space and sets new state-of-the-art results on most evaluated metrics, especially under strict pose thresholds.
Achieving relightable UV-PBR mesh assets from multi-view images could revolutionize the way digital content is created and manipulated.
Adversarial images in CLIP reveal a consistent directional bias that can be exploited to enhance model robustness, leading to surprising cases where adversarial accuracy surpasses clean accuracy.
Achieve state-of-the-art category-level object pose estimation by unifying shape completion and pose reasoning, without relying on category-level shape priors.
