Abstract. There has been significant progress on pose estimation and
increasing interests on pose tracking in recent years. At the same time,
the overall algorithm and system complexity increases as well, making
the algorithm analysis and comparison more difficult. This work provides
simple and effective baseline methods. They are helpful for inspiring and
evaluating new ideas for the field. State-of-the-art results are achieved on
challenging benchmarks. The code will be available at https://github.
com/leoxiaobin/pose.pytorch