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Hierarchical part-based disentanglement of pose and appearance

University of British Columbia

Landmarks and keypoints are an important intermediate representation for image understanding and reconstruction. Although, many supervised approaches exist, these require labels of the target domain, which exist for humans, but only for sparse keypoints and not for the breadth of object and animal classes present in our rich world. We propose a self-supervised approach for discovering landmarks from unstructured image collections by disentangling pose and appearance of object parts. In particular, we propose a hierarchical structure that helps to find more meaningful keypoint locations. We demonstrate that our simplifications and hierarchical extensions of prior work are effective, in terms of quantitative 2D keypoint estimation and qualitative image modification operations when applied to persons. Our approach eases the discovery of objects and their parts in domains for which no labeled data exist and thereby eases downstream tasks, such as keypoint estimation, behavior classification for neuroscience applications, and intuitive image editing.

Text

2020-12-18

Attribution-NoDerivatives 4.0 International

http://hdl.handle.net/2429/76854

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nd/4.0/