3260 papers • 126 benchmarks • 313 datasets
The discovery of object landmarks on a set of images depicting objects of the same category, directly from raw images without using any manual annotations.
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This paper combines a non-rigid 3D neural prior with deep flow to obtain high-fidelity landmark estimates from videos with only two or three uncalibrated, handheld cameras, and produces 2D results comparable to state-of-the-art fully supervised methods, along with 3D reconstructions that are impossible with other existing approaches.
By building upon an existing structured representation learned in a supervised manner, the optimization problem solved by the method is much more constrained with significantly less parameters to learn which seems to be important for the case of unsupervised learning.
Although simpler, AutoLink outperforms existing self-supervised methods on the established keypoint and pose estimation benchmarks and paves the way for structure-conditioned generative models on more diverse datasets.
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