  We propose a personalized ConvNet pose estimator that automatically adapts
itself to the uniqueness of a person's appearance to improve pose estimation in
long videos. We make the following contributions: (i) we show that given a few
high-precision pose annotations, e.g. from a generic ConvNet pose estimator,
additional annotations can be generated throughout the video using a
combination of image-based matching for temporally distant frames, and dense
optical flow for temporally local frames; (ii) we develop an occlusion aware
self-evaluation model that is able to automatically select the high-quality and
reject the erroneous additional annotations; and (iii) we demonstrate that
these high-quality annotations can be used to fine-tune a ConvNet pose
estimator and thereby personalize it to lock on to key discriminative features
of the person's appearance. The outcome is a substantial improvement in the
pose estimates for the target video using the personalized ConvNet compared to
the original generic ConvNet. Our method outperforms the state of the art
(including top ConvNet methods) by a large margin on two standard benchmarks,
as well as on a new challenging YouTube video dataset. Furthermore, we show
that training from the automatically generated annotations can be used to
improve the performance of a generic ConvNet on other benchmarks.
