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Abt. Black
Abstract:
We advocate the inference of qualitative information about 3D human
pose, called posebits, from images. Posebits represent boolean
geometric relationships between body parts
(e.g., left-leg in front of right-leg or hands close to each other).
The advantages of posebits as a mid-level representation are 1)
for many tasks of interest, such qualitative
pose information may be sufficient (e.g. \, semantic image retrieval),
2) it is relatively easy to annotate large image corpora with posebits, as
it simply requires answers to yes/no questions; and 3) they help
resolve challenging pose ambiguities and therefore
facilitate the difficult talk of image-based 3D pose estimation.
We introduce posebits, a posebit database, a method for selecting useful
posebits for pose estimation and a structural SVM model for posebit inference.
Experiments show the use of posebits for semantic image
retrieval and for improving 3D pose estimation.
