  This is the project report for CSCI-GA.2271-001. We target human pose
estimation in artistic images. For this goal, we design an end-to-end system
that uses neural style transfer for pose regression. We collect a 277-style set
for arbitrary style transfer and build an artistic 281-image test set. We
directly run pose regression on the test set and show promising results. For
pose regression, we propose a 2d-induced bone map from which pose is lifted. To
help such a lifting, we additionally annotate the pseudo 3d labels of the full
in-the-wild MPII dataset. Further, we append another style transfer as self
supervision to improve 2d. We perform extensive ablation studies to analyze the
introduced features. We also compare end-to-end with per-style training and
allude to the tradeoff between style transfer and pose regression. Lastly, we
generalize our model to the real-world human dataset and show its potentiality
as a generic pose model. We explain the theoretical foundation in Appendix. We
release code at https://github.com/strawberryfg/NAPA-NST-HPE, data, and video.
