  We study the problem of estimating 3D shape and pose of an object in terms of
keypoints, from a single 2D image.
  The shape and pose are learned directly from images collected by categories
and their partial 2D keypoint annotations.. In this work, we first propose an
end-to-end training framework for intermediate 2D keypoints extraction and
final 3D shape and pose estimation. The proposed framework is then trained
using only the weak supervision of the intermediate 2D keypoints. Additionally,
we devise a semi-supervised training framework that benefits from both labeled
and unlabeled data. To leverage the unlabeled data, we introduce and exploit
the \emph{piece-wise planar hull} prior of the canonical object shape. These
planar hulls are defined manually once per object category, with the help of
the keypoints. On the one hand, the proposed method learns to segment these
planar hulls from the labeled data. On the other hand, it simultaneously
enforces the consistency between predicted keypoints and the segmented hulls on
the unlabeled data. The enforced consistency allows us to efficiently use the
unlabeled data for the task at hand. The proposed method achieves comparable
results with fully supervised state-of-the-art methods by using only half of
the annotations. Our source code will be made publicly available.
